今天在做一个网页时，在IE7下发现一个奇怪的问题，先看代码：

<div style="float: left;">
	<img id="pic1" src="images/picture1.jpg" style="border:4px solid #191919;" 
		width="553" height="179" />
</div>

 感觉代码没问题，可是预览时总是发现图片下方和div边框之间有一条大概4px的空隙，最后还是得以解决。

最简单的办法是，标签写在一行中：

<div style="width: 900px;height: 75px;margin-top: 0px;"><img src="images/top.jpg" id="top" width="900" height="75"></div>

其他办法主要还是靠css属性解决! 不过，有些属性调整后在firefox下可能会有问题。一个可以使用display属性解决：

<div style="float: left;">
	<img id="pic1" src="images/picture1.jpg" style="border:4px solid #191919;display:block;" 
		width="553" height="179" />
</div>

另一个可以用vertical-align属性解决：

<div style="float: left;">
	<img id="pic1" src="images/picture1.jpg" style="border:4px solid #191919;vertical-align:top;" 
		width="553" height="179" />
</div>

  使用margin-bottom属性解决：

<div style="width: 900px;height: 75px;margin-top: 0px;">
	<img src="images/top.jpg" style="margin-bottom: -4px;" id="top" width="900" height="75">
</div>

 使用字体大小属性font-size解决：

<div style="width: 900px;height: 75px;margin-top: 0px;font-size:0">
	<img src="images/top.jpg" id="top" width="900" height="75">
</div>

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var a = function(){......}();

add = function(){
       return 2+3;
}
var _i = add();

var _i = function(){return 2+3;}();

add = function(a,b){
           return a+b;
}
var _i = add(2,3);

var _i = function(2,3){return a+b;}();

var _i = function(){return add(2,5);}();

var _i = function(){return new Function("return add(2,5);")}()();

var _i = function(){
            return {add:function(a,b){return a+b;}}；
}();
alert(_i.add(2,3));

var _i = function(){
	return {
		add:function(a,b){return a+b;},
		minus:function(){return _i.add(2,3)-1;}
	};
}();
alert(_i.minus()));

• Oracle专区上线，有Oracle最新文章，重要下载及知识库等精彩内容，欢迎访问。
• 搜狐网站诚聘Java、PHP和C++工程师
• JavaEye问答大赛开始了！ 从6月23日 至 7月6日，奖品丰厚 ！
• 北京: 千橡集团暨校内网诚聘软件研发工程师
• 快来参加7月17日在成都举行的SOA中国技术论坛

ClusterMain.java

package eu.eodigos.kmean;

import java.util.Iterator;
import java.util.List;
import java.util.Vector;

import eu.eodigos.hibernate.bean.ClusterInput;
import eu.eodigos.hibernate.server.AccessDBServer;
import eu.eodigos.hibernate.server.AccessDBServerImp;

/**
 * @author daoger
 * @version 1.0
 * @k-mean Cluster
 */

public class ClusterMain
{
	public static void main(String[] args)
	{
		ClusterMain clusterMain = new ClusterMain();
		clusterMain.clusterByDatabase();
	}

	/**
	 * Test with manual data
	 */
	public void test()
	{
		Vector<DataPoint> dataPoints = new Vector<DataPoint>();
		dataPoints.add(new DataPoint(0.12, 0.21, 0.26, 0.45, 0.67, 0.23, 0.11, new Integer(1)));
		dataPoints.add(new DataPoint(0.22, 0.23, 0.46, 0.11, 0.63, 0.11, 0.12, new Integer(2)));
		dataPoints.add(new DataPoint(0.32, 0.34, 0.78, 0.17, 0.68, 0.67, 0.13, new Integer(3)));
		dataPoints.add(new DataPoint(0.42, 0.45, 0.26, 0.42, 0.48, 0.39, 0.14, new Integer(4)));
		dataPoints.add(new DataPoint(0.52, 0.29, 0.65, 0.59, 0.16, 0.74, 0.15, new Integer(5)));
		dataPoints.add(new DataPoint(0.62, 0.25, 0.48, 0.61, 0.27, 0.16, 0.67, new Integer(6)));
		dataPoints.add(new DataPoint(0.72, 0.35, 0.39, 0.20, 0.65, 0.26, 0.17, new Integer(7)));
		dataPoints.add(new DataPoint(0.82, 0.20, 0.16, 0.29, 0.32, 0.61, 0.18, new Integer(8)));
		dataPoints.add(new DataPoint(0.92, 0.71, 0.26, 0.37, 0.17, 0.81, 0.19, new Integer(9)));
		dataPoints.add(new DataPoint(0.13, 0.39, 0.17, 0.41, 0.47, 0.37, 0.10, new Integer(10)));
		dataPoints.add(new DataPoint(0.14, 0.23, 0.47, 0.93, 0.68, 0.28, 0.29, new Integer(11)));
		dataPoints.add(new DataPoint(0.15, 0.57, 0.84, 0.19, 0.15, 0.39, 0.39, new Integer(12)));
		dataPoints.add(new DataPoint(0.16, 0.19, 0.45, 0.38, 0.36, 0.82, 0.49, new Integer(13)));
		dataPoints.add(new DataPoint(0.17, 0.89, 0.29, 0.39, 0.82, 0.58, 0.59, new Integer(14)));
		// divide all user to 7 cluster
		// 10000 stand for precision,the bigger of this value the more accuratly
		ClusterAssistant clusterAssistant = new ClusterAssistant(3, 10000, dataPoints);
		clusterAssistant.startAnalysis();

		Vector[] v = clusterAssistant.getClusterOutput();
		for (int i = 0; i < v.length; i++)
		{
			Vector tempV = v[i];
			System.out.println("-----------Cluster" + i + "---------");
			Iterator iter = tempV.iterator();
			while (iter.hasNext())
			{
				DataPoint dpTemp = (DataPoint) iter.next();
				String dps = "userid_" + dpTemp.getUserid() + "[" + dpTemp.getAvg1() + "," + dpTemp.getAvg2()
						+ dpTemp.getAvg3() + "," + dpTemp.getAvg4() + "," + dpTemp.getAvg5() + "," + dpTemp.getAvg6()
						+ "," + dpTemp.getAvg7() + "]";
				System.out.println(dps);
			}
		}
	}

	/**
	 * get data from database and calaulate
	 */
	public void clusterByDatabase()
	{
		AccessDBServer access = new AccessDBServerImp();
		Vector<DataPoint> dataPoints = new Vector<DataPoint>();
		List clusterList = access.getAllClusterInputData();
		for (Iterator iter = clusterList.iterator(); iter.hasNext();)
		{
			ClusterInput clusterInput = (ClusterInput) iter.next();
			if (clusterInput != null)
			{
				dataPoints.add(new DataPoint(clusterInput.getAvgArch(), clusterInput.getAvgMon(), clusterInput
						.getAvgMus(), clusterInput.getAvgBuil(), clusterInput.getAvgChap(), clusterInput.getAvgBeach(),
						clusterInput.getAvgWalk(), clusterInput.getClusterId()));
			}

		}
		// divide all user to 7 cluster
		// 10000 stand for precision,the bigger of this value the more accuratly
		ClusterAssistant clusterAssistant = new ClusterAssistant(7, 10000, dataPoints);
		clusterAssistant.startAnalysis();

		Vector[] v = clusterAssistant.getClusterOutput();
		for (int i = 0; i < v.length; i++)
		{
			Vector tempV = v[i];
			Iterator iter = tempV.iterator();
			while (iter.hasNext())
			{
				DataPoint dpTemp = (DataPoint) iter.next();
				access.updateClusterCateOfUsers(dpTemp.getUserid(), new Integer(i + 1));
			}
		}
	}
}

 ClusterAssistant.java

package eu.eodigos.kmean;

import java.util.Vector;

/**
 * @author daoger
 * @version 1.0
 * @k-mean Cluster
 */


public class ClusterAssistant
{
	private Cluster[] clusters;

	private int miter;

	private Vector mDataPoints = new Vector();

	private double mSWCSS;

	public ClusterAssistant(int k, int iter, Vector dataPoints)
	{
		clusters = new Cluster[k];
		for (int i = 0; i < k; i++)
		{
			clusters[i] = new Cluster("Cluster" + i);
		}
		this.miter = iter;
		this.mDataPoints = dataPoints;
	}

	private void calcSWCSS()
	{
		double temp = 0;
		for (int i = 0; i < clusters.length; i++)
		{
			temp = temp + clusters[i].getSumSqr();
		}
		mSWCSS = temp;
	}

	public void startAnalysis()
	{
		setInitialCentroids();
		int n = 0;
		loop1: while (true)
		{
			for (int l = 0; l < clusters.length; l++)
			{
				clusters[l].addDataPoint((DataPoint) mDataPoints.elementAt(n));
				n++;
				if (n >= mDataPoints.size())
					break loop1;
			}
		}
		calcSWCSS();
		for (int i = 0; i < clusters.length; i++)
		{
			clusters[i].getCentroid().calcCentroid();
		}
		calcSWCSS();

		for (int i = 0; i < miter; i++)
		{
			for (int j = 0; j < clusters.length; j++)
			{
				for (int k = 0; k < clusters[j].getNumDataPoints(); k++)
				{
					double tempEuDt = clusters[j].getDataPoint(k).getCurrentEuDt();
					Cluster tempCluster = null;
					boolean matchFoundFlag = false;

					for (int l = 0; l < clusters.length; l++)
					{

						if (tempEuDt > clusters[j].getDataPoint(k).testEuclideanDistance(clusters[l].getCentroid()))
						{
							tempEuDt = clusters[j].getDataPoint(k).testEuclideanDistance(clusters[l].getCentroid());
							tempCluster = clusters[l];
							matchFoundFlag = true;
						}
					}
					if (matchFoundFlag)
					{
						tempCluster.addDataPoint(clusters[j].getDataPoint(k));
						clusters[j].removeDataPoint(clusters[j].getDataPoint(k));
						for (int m = 0; m < clusters.length; m++)
						{
							clusters[m].getCentroid().calcCentroid();
						}
						calcSWCSS();
					}
				}
			}
		}
	}

	public Vector[] getClusterOutput()
	{
		Vector v[] = new Vector[clusters.length];
		for (int i = 0; i < clusters.length; i++)
		{
			v[i] = clusters[i].getDataPoints();
		}
		return v;
	}

	private void setInitialCentroids()
	{
		// kn = (round((max-min)/k)*n)+min where n is from 0 to (k-1).
		double[] c = new double[7];
		for (int n = 1; n <= clusters.length; n++)
		{
			for (int i = 1; i < 8; i++)
			{
				c[i - 1] = (((getMaxXValue(i) - getMinXValue(i)) / (clusters.length + 1)) * n) + getMinXValue(i);
			}
			Centroid ce = new Centroid(c[0], c[1], c[2], c[3], c[4], c[5], c[6]);
			clusters[n - 1].setCentroid(ce);
			ce.setCluster(clusters[n - 1]);
		}
	}

	private double getMaxXValue(int avgnumber)
	{
		double temp = 0.0;
		switch (avgnumber)
		{
		case 1:// Archeological
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg1();
			break;
		case 2:// Monuments
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg2();
			break;
		case 3:// Museums
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg3();
			break;
		case 4:// Buildings
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg4();
			break;
		case 5:// Chapels
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg5();
			break;
		case 6:// Beaches
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg6();
			break;
		case 7:// Walking
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg7();
			break;
		}
		for (int i = 0; i < mDataPoints.size(); i++)
		{
			DataPoint dp = (DataPoint) mDataPoints.elementAt(i);
			switch (avgnumber)
			{
			case 1:// Archeological
				temp = (dp.getAvg1() > temp) ? dp.getAvg1() : temp;
				break;
			case 2:// Monuments
				temp = (dp.getAvg2() > temp) ? dp.getAvg2() : temp;
				break;
			case 3:// Museums
				temp = (dp.getAvg3() > temp) ? dp.getAvg3() : temp;
				break;
			case 4:// Buildings
				temp = (dp.getAvg4() > temp) ? dp.getAvg4() : temp;
				break;
			case 5:// Chapels
				temp = (dp.getAvg5() > temp) ? dp.getAvg5() : temp;
				break;
			case 6:// Beaches
				temp = (dp.getAvg6() > temp) ? dp.getAvg6() : temp;
				break;
			case 7:// Walking
				temp = (dp.getAvg7() > temp) ? dp.getAvg7() : temp;
				break;
			}
		}
		return temp;
	}

	private double getMinXValue(int avgnumber)
	{
		double temp = 0.0;
		switch (avgnumber)
		{
		case 1:// Archeological
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg1();
			break;
		case 2:// Monuments
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg2();
			break;
		case 3:// Museums
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg3();
			break;
		case 4:// Buildings
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg4();
			break;
		case 5:// Chapels
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg5();
			break;
		case 6:// Beaches
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg6();
			break;
		case 7:// Walking
			temp = ((DataPoint) mDataPoints.elementAt(0)).getAvg7();
			break;
		}
		for (int i = 0; i < mDataPoints.size(); i++)
		{
			DataPoint dp = (DataPoint) mDataPoints.elementAt(i);
			switch (avgnumber)
			{
			case 1:// Archeological
				temp = (dp.getAvg1() < temp) ? dp.getAvg1() : temp;
				break;
			case 2:// Monuments
				temp = (dp.getAvg2() < temp) ? dp.getAvg2() : temp;
				break;
			case 3:// Museums
				temp = (dp.getAvg3() < temp) ? dp.getAvg3() : temp;
				break;
			case 4:// Buildings
				temp = (dp.getAvg4() < temp) ? dp.getAvg4() : temp;
				break;
			case 5:// Chapels
				temp = (dp.getAvg5() < temp) ? dp.getAvg5() : temp;
				break;
			case 6:// Beaches
				temp = (dp.getAvg6() < temp) ? dp.getAvg6() : temp;
				break;
			case 7:// Walking
				temp = (dp.getAvg7() < temp) ? dp.getAvg7() : temp;
				break;
			}
		}
		return temp;
	}

	public int getKValue()
	{
		return clusters.length;
	}

	public int getIterations()
	{
		return miter;
	}

	public int getTotalDataPoints()
	{
		return mDataPoints.size();
	}

	public double getSWCSS()
	{
		return mSWCSS;
	}

	public Cluster getCluster(int pos)
	{
		return clusters[pos];
	}
}

Centroid.java

package eu.eodigos.kmean;

/**
 * @author daoger
 * @version 1.0
 * @k-mean Cluster
 */

class Centroid
{
	private double avgC1, avgC2, avgC3, avgC4, avgC5, avgC6, avgC7;

	private Cluster mCluster;

	public Centroid(double ac1, double ac2, double ac3, double ac4, double ac5, double ac6, double ac7)
	{
		this.avgC1 = ac1;
		this.avgC2 = ac2;
		this.avgC3 = ac3;
		this.avgC4 = ac4;
		this.avgC5 = ac5;
		this.avgC6 = ac6;
		this.avgC7 = ac7;
	}

	public void calcCentroid()
	{ // only called by CAInstance
		int numDP = mCluster.getNumDataPoints();
		double temp1 = 0, temp2 = 0, temp3 = 0, temp4 = 0, temp5 = 0, temp6 = 0, temp7 = 0;
		int i;
		// caluclating the new Centroid
		for (i = 0; i < numDP; i++)
		{
			temp1 = temp1 + mCluster.getDataPoint(i).getAvg1();
			// total for avg1
			temp2 = temp2 + mCluster.getDataPoint(i).getAvg2();
			// total for avg1
			temp3 = temp3 + mCluster.getDataPoint(i).getAvg3();
			// total for avg1
			temp4 = temp4 + mCluster.getDataPoint(i).getAvg4();
			// total for avg1
			temp5 = temp5 + mCluster.getDataPoint(i).getAvg5();
			// total for avg1
			temp6 = temp6 + mCluster.getDataPoint(i).getAvg6();
			// total for avg1
			temp7 = temp7 + mCluster.getDataPoint(i).getAvg7();
			// total for avg1
		}
		this.avgC1 = temp1 / numDP;
		this.avgC2 = temp2 / numDP;
		this.avgC3 = temp3 / numDP;
		this.avgC4 = temp4 / numDP;
		this.avgC5 = temp5 / numDP;
		this.avgC6 = temp6 / numDP;
		this.avgC7 = temp7 / numDP;
		// calculating the new Euclidean Distance for each Data Point
		temp1 = 0;
		temp2 = 0;
		temp3 = 0;
		temp4 = 0;
		temp5 = 0;
		temp6 = 0;
		temp7 = 0;
		for (i = 0; i < numDP; i++)
		{
			mCluster.getDataPoint(i).calcEuclideanDistance();
		}
		// calculate the new Sum of Squares for the Cluster
		mCluster.calcSumOfSquares();
	}

	public void setCluster(Cluster c)
	{
		this.mCluster = c;
	}

	public double getAvgC1()
	{
		return avgC1;
	}

	public void setAvgC1(double avgC1)
	{
		this.avgC1 = avgC1;
	}

	public double getAvgC2()
	{
		return avgC2;
	}

	public void setAvgC2(double avgC2)
	{
		this.avgC2 = avgC2;
	}

	public double getAvgC3()
	{
		return avgC3;
	}

	public void setAvgC3(double avgC3)
	{
		this.avgC3 = avgC3;
	}

	public double getAvgC4()
	{
		return avgC4;
	}

	public void setAvgC4(double avgC4)
	{
		this.avgC4 = avgC4;
	}

	public double getAvgC5()
	{
		return avgC5;
	}

	public void setAvgC5(double avgC5)
	{
		this.avgC5 = avgC5;
	}

	public double getAvgC6()
	{
		return avgC6;
	}

	public void setAvgC6(double avgC6)
	{
		this.avgC6 = avgC6;
	}

	public double getAvgC7()
	{
		return avgC7;
	}

	public void setAvgC7(double avgC7)
	{
		this.avgC7 = avgC7;
	}

	public Cluster getCluster()
	{
		return mCluster;
	}

}

Cluster.java

package eu.eodigos.kmean;

import java.util.Vector;

/**
 * @author daoger
 * @version 1.0
 * @k-mean Cluster
 */


class Cluster
{
	private String mName;

	private Centroid mCentroid;

	private double mSumSqr;

	private Vector<DataPoint> mDataPoints;

	public Cluster(String name)
	{
		this.mName = name;
		this.mCentroid = null; // will be set by calling setCentroid()
		mDataPoints = new Vector<DataPoint>();
	}

	public void setCentroid(Centroid c)
	{
		mCentroid = c;
	}

	public Centroid getCentroid()
	{
		return mCentroid;
	}

	public void addDataPoint(DataPoint dp) { // called from CAInstance
        dp.setCluster(this); // initiates a inner call to calcEuclideanDistance() in DP.
        this.mDataPoints.addElement(dp);
        calcSumOfSquares();
    }

	public void removeDataPoint(DataPoint dp)
	{
		this.mDataPoints.removeElement(dp);
		calcSumOfSquares();
	}

	public int getNumDataPoints()
	{
		return this.mDataPoints.size();
	}

	public DataPoint getDataPoint(int pos)
	{
		return (DataPoint) this.mDataPoints.elementAt(pos);
	}

	public void calcSumOfSquares()
	{ // called from Centroid
		int size = this.mDataPoints.size();
		double temp = 0;
		for (int i = 0; i < size; i++)
		{
			temp = temp + ((DataPoint) this.mDataPoints.elementAt(i)).getCurrentEuDt();
		}
		this.mSumSqr = temp;
	}

	public double getSumSqr()
	{
		return this.mSumSqr;
	}

	public String getName()
	{
		return this.mName;
	}

	public Vector getDataPoints()
	{
		return this.mDataPoints;
	}

}

DataPoint.java

package eu.eodigos.kmean;

/**
 * @author daoger
 * @version 1.0
 * @k-mean Cluster
 */

public class DataPoint
{
	private double avg1, avg2, avg3, avg4, avg5, avg6, avg7;

	private Integer userid;

	private Cluster mCluster;

	private double mEuDt;

	public DataPoint(double avg1, double avg2, double avg3, double avg4, double avg5, double avg6, double avg7,
			Integer userid)
	{
		this.avg1 = avg1;
		this.avg2 = avg2;
		this.avg3 = avg3;
		this.avg4 = avg4;
		this.avg5 = avg5;
		this.avg6 = avg6;
		this.avg7 = avg7;
		this.userid = userid;
		this.mCluster = null;
	}

	public void setCluster(Cluster cluster)
	{
		this.mCluster = cluster;
		calcEuclideanDistance();
	}

	public void calcEuclideanDistance()
	{

		// called when DP is added to a cluster or when a Centroid is
		// recalculated.
		mEuDt = Math.sqrt(Math.pow((avg1 - mCluster.getCentroid().getAvgC1()), 2)
				+ Math.pow((avg2 - mCluster.getCentroid().getAvgC2()), 2)
				+ Math.pow((avg3 - mCluster.getCentroid().getAvgC3()), 2)
				+ Math.pow((avg4 - mCluster.getCentroid().getAvgC4()), 2)
				+ Math.pow((avg5 - mCluster.getCentroid().getAvgC5()), 2)
				+ Math.pow((avg6 - mCluster.getCentroid().getAvgC6()), 2)
				+ Math.pow((avg7 - mCluster.getCentroid().getAvgC7()), 2));
	}

	public double testEuclideanDistance(Centroid c)
	{
		return Math.sqrt(Math.pow((avg1 - c.getAvgC1()), 2) + Math.pow((avg2 - c.getAvgC2()), 2)
				+ Math.pow((avg3 - c.getAvgC3()), 2) + Math.pow((avg4 - c.getAvgC4()), 2)
				+ Math.pow((avg5 - c.getAvgC5()), 2) + Math.pow((avg6 - c.getAvgC6()), 2)
				+ Math.pow((avg7 - c.getAvgC7()), 2));
	}

	public double getAvg1()
	{
		return avg1;
	}

	public void setAvg1(double avg1)
	{
		this.avg1 = avg1;
	}

	public double getAvg2()
	{
		return avg2;
	}

	public void setAvg2(double avg2)
	{
		this.avg2 = avg2;
	}

	public double getAvg3()
	{
		return avg3;
	}

	public void setAvg3(double avg3)
	{
		this.avg3 = avg3;
	}

	public double getAvg4()
	{
		return avg4;
	}

	public void setAvg4(double avg4)
	{
		this.avg4 = avg4;
	}

	public double getAvg5()
	{
		return avg5;
	}

	public void setAvg5(double avg5)
	{
		this.avg5 = avg5;
	}

	public double getAvg6()
	{
		return avg6;
	}

	public void setAvg6(double avg6)
	{
		this.avg6 = avg6;
	}

	public double getAvg7()
	{
		return avg7;
	}

	public void setAvg7(double avg7)
	{
		this.avg7 = avg7;
	}

	public Cluster getCluster()
	{
		return mCluster;
	}

	public double getCurrentEuDt()
	{
		return mEuDt;
	}

	/**
	 * @return the userid
	 */
	public Integer getUserid()
	{
		return userid;
	}

	/**
	 * @param userid
	 *            the userid to set
	 */
	public void setUserid(Integer userid)
	{
		this.userid = userid;
	}

}

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• Marketing: finding groups of customers with similar behavior given a large database of customer data containing their properties and past buying records;
• Biology: classification of plants and animals given their features;
• Libraries: book ordering;
• Insurance: identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds;
• City-planning: identifying groups of houses according to their house type, value and geographical location;
• Earthquake studies: clustering observed earthquake epicenters to identify dangerous zones;
• WWW: document classification; clustering weblog data to discover groups of similar access patterns.

• scalability;
• dealing with different types of attributes;
• discovering clusters with arbitrary shape;
• minimal requirements for domain knowledge to determine input parameters;
• ability to deal with noise and outliers;
• insensitivity to order of input records;
• high dimensionality;
• interpretability and usability.

• current clustering techniques do not address all the requirements adequately (and concurrently);
• dealing with large number of dimensions and large number of data items can be problematic because of time complexity;
• the effectiveness of the method depends on the definition of “distance” (for distance-based clustering);
• if an obvious distance measure doesn’t exist we must “define” it, which is not always easy, especially in multi-dimensional spaces;
• the result of the clustering algorithm (that in many cases can be arbitrary itself) can be interpreted in different ways.

• Exclusive Clustering
• Overlapping Clustering
• Hierarchical Clustering
• Probabilistic Clustering

• K-means
• Fuzzy C-means
• Hierarchical clustering
• Mixture of Gaussians

Each of these algorithms belongs to one of the clustering types listed above. So that, K-means is an exclusive clustering algorithm, Fuzzy C-means is an overlapping clustering algorithm, Hierarchical clustering is obvious and lastly Mixture of Gaussian is a probabilistic clustering algorithm. We will discuss about each clustering method in the following paragraphs.

Distance Measure
An important component of a clustering algorithm is the distance measure between data points. If the components of the data instance vectors are all in the same physical units then it is possible that the simple Euclidean distance metric is sufficient to successfully group similar data instances. However, even in this case the Euclidean distance can sometimes be misleading. Figure shown below illustrates this with an example of the width and height measurements of an object. Despite both measurements being taken in the same physical units, an informed decision has to be made as to the relative scaling. As the figure shows, different scalings can lead to different clusterings.

Notice however that this is not only a graphic issue: the problem arises from the mathematical formula used to combine the distances between the single components of the data feature vectors into a unique distance measure that can be used for clustering purposes: different formulas leads to different clusterings.
Again, domain knowledge must be used to guide the formulation of a suitable distance measure for each particular application.

Minkowski Metric
For higher dimensional data, a popular measure is the Minkowski metric,

where d is the dimensionality of the data. The Euclidean distance is a special case where p=2, while Manhattan metric has p=1. However, there are no general theoretical guidelines for selecting a measure for any given application.

It is often the case that the components of the data feature vectors are not immediately comparable. It can be that the components are not continuous variables, like length, but nominal categories, such as the days of the week. In these cases again, domain knowledge must be used to formulate an appropriate measure.

Bibliography

• Tariq Rashid: “Clustering”
  http://www.cs.bris.ac.uk/home/tr1690/documentation/fuzzy_clustering_initial_report/node11.html
• Osmar R. Zaïane: “Principles of Knowledge Discovery in Databases - Chapter 8: Data Clustering”
  http://www.cs.ualberta.ca/~zaiane/courses/cmput690/slides/Chapter8/index.html
• Pier Luca Lanzi: “Ingegneria della Conoscenza e Sistemi Esperti – Lezione 2: Apprendimento non supervisionato”
  http://www.elet.polimi.it/upload/lanzi/corsi/icse/2002/Lezione%202%20-%20Apprendimento%20non%20supervisionato.pdf

来自于：http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/index.html

Next Page

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<mx:request>
      <txtPara>{txtPara.text}</txtPara>
 </mx:request>

<?xml version="1.0" encoding="utf-8"?>
<mx:Application xmlns:mx="http://www.adobe.com/2006/mxml" layout="absolute" >
	<mx:Script>  
      <![CDATA[  
        import mx.rpc.events.ResultEvent;              
		import mx.controls.Alert;                      
		private function httpHandle(e:ResultEvent):void 
		{
			Alert.show(e.result.Result);
		}
        ]]>  
  	</mx:Script>  
	<mx:HTTPService id="myHttp" url="http://localhost:8080/myflex/http" showBusyCursor="true" result="httpHandle(event);" useProxy="false">
		<mx:request>
         	<txtPara>{txtPara.text}</txtPara>
    	</mx:request>
	</mx:HTTPService>
	<mx:Panel title="TEST HTTPService" width="368" height="140" x="78" y="30" layout="absolute">
	   	<mx:Label text="PARA" x="110" y="26"/>  
	    <mx:TextInput id="txtPara" x="161" y="24" width="95"/> 
	    <mx:Label text="The para sent to service is:" x="58" y="53"/>
	    <mx:Label x="126" y="53" id="lblResult"/>  
	    <mx:Button label="Submit" click="myHttp.send()" x="277" y="53"/>   
	</mx:Panel>
</mx:Application>

<?xml version="1.0" encoding="utf-8"?>
<!-- Simple example to demonstrate Form layout container. -->
<mx:Application xmlns:mx="http://www.adobe.com/2006/mxml">
    <mx:Panel title="Form Container Example" height="75%" width="75%" 
        paddingTop="10" paddingLeft="10" paddingRight="10" paddingBottom="10">
        <mx:Text width="100%" color="blue"
            text="Moving from one form field to another triggers the validator."/>
        <mx:Form width="100%" height="100%">
            <mx:FormHeading label="Enter values into the form."/>
            <mx:FormItem label="First name">
                <mx:TextInput id="fname" width="200"/>
            </mx:FormItem>
            <mx:FormItem label="Date of birth (mm/dd/yyyy)">
                <mx:TextInput id="dob" width="200"/>
            </mx:FormItem>
            <mx:FormItem label="E-mail address">
                <mx:TextInput id="email" width="200"/>
            </mx:FormItem>
            <mx:FormItem label="Age">
                <mx:TextInput id="age" width="200"/>
            </mx:FormItem>
            <mx:FormItem label="SSN">
                <mx:TextInput id="ssn" width="200"/>
            </mx:FormItem>
            <mx:FormItem label="Zip">
                <mx:TextInput id="zip" width="200"/>
            </mx:FormItem>
            <mx:FormItem label="Phone">
                <mx:TextInput id="phone" width="200"/>
            </mx:FormItem>
        </mx:Form>
    </mx:Panel>

    <mx:StringValidator source="{fname}" property="text" minLength="4" maxLength="12"/>
    <mx:PhoneNumberValidator source="{phone}" property="text"/>
    <mx:DateValidator source="{dob}" property="text"/>
    <mx:EmailValidator source="{email}" property="text"/>
    <mx:NumberValidator source="{age}" property="text" integerError="Enter Integer value"
        minValue="18" maxValue="100" domain="int"/>
    <mx:SocialSecurityValidator source="{ssn}" property="text"/>
    <mx:ZipCodeValidator source="{zip}" property="text"/>
</mx:Application>

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<?xml version="1.0" encoding="utf-8"?>
<menus>
    <node label="Mail">
        <node label="Inbox"/>
        <node label="Personal Folder">
            <node label="Demo"/>
            <node label="Personal"/>
            <node label="Saved Mail"/>
            <node label="bar"/>
        </node>
        <node label="Calendar"/>
        <node label="Sent"/>
        <node label="Trash"/>
    </node>
</menus>

   var testXML:XML;
   var file:File = File.documentsDirectory.resolvePath("tree.xml");
   var fileStream:FileStream = new FileStream();
   fileStream.open(file, FileMode.READ);
   testXML = XML(fileStream.readUTFBytes(fileStream.bytesAvailable));
   fileStream.close();

   var testXML:XML=<content>content</content>......;
   var file:File = File.documentsDirectory.resolvePath("tree.xml");
   var fileStream:FileStream = new FileStream();
   fileStream.open(file, FileMode.WRITE);
   var outputString:String = '<?xml version="1.0" encoding="utf-8"?>\n';
   outputString += testXML.toXMLString();
   fileStream.writeUTFBytes(outputString);
   fileStream.close();

<?xml version="1.0" encoding="utf-8"?>
<mx:Application xmlns:mx="http://www.adobe.com/2006/mxml" fontFamily="simsun" 
	fontSize="12" layout="absolute" width="242" height="442" creationComplete="loadXML()">
    <mx:Script>
          <![CDATA[
            import mx.collections.ArrayCollection; 
            import mx.rpc.events.ResultEvent;       
            public var loader:URLLoader=new URLLoader();
            public var menus:XML=new XML();         
            public function loadXML():void          
            {
                var request:URLRequest=new URLRequest("tree.xml");
                loader.load(request); 
                loader.addEventListener(Event.COMPLETE,completeHandle);
            }
            public function completeHandle(e:Event):void
            {
                menus=XML(loader.data);            
                var results:XMLList=menus.node;    
                tree.dataProvider=results; 
            }
          ]]>
    </mx:Script>

    <mx:Tree id="tree" x="10" y="35" width="218" height="397" labelField="@label" />
    <mx:Label x="10" y="10" text="Tree Nodes From XML File"/>
 </mx:Application>

import mx.rpc.events.ResultEvent;                       
import mx.controls.Alert;                              

private function showResult(e:ResultEvent):void
{
    Alert.show(e.result as String);                    
}

<?xml version="1.0" encoding="utf-8"?>
<mx:Application xmlns:mx="http://www.adobe.com/2006/mxml" layout="absolute" >
	<mx:Script>  
      <![CDATA[  
        import mx.rpc.events.ResultEvent;              
		import mx.controls.Alert;                   
		
		private var arr:Array=new Array();             
		private function addHandle():void           
		{
		    myHttp.url="http://localhost:8080/myflex/sum";                                                     
		    if(arr.length>0)                           
		        myHttp.url+="?";
		    for(var i:int=0;i<arr.length;i++)           
		    {
		        if(i!=arr.length-1)
		            myHttp.url+="num="+arr[i].para.toString()+"&";
		        else
		            myHttp.url+="num="+arr[i].para.toString();
		    }
		    Alert.show(myHttp.url);
		    myHttp.send();                       
		}
		
		private function addData():void         
		{
		    var obj:Object=new Object();           
		    obj.para=txtPara.text;                
		    arr.push(obj);                        
		    dg.dataProvider=arr;                   
		    txtPara.text="";                     
		    dg.validateNow();                      
		}
		
		private function delData():void         
		{
		    arr=new Array();                             
		    dg.dataProvider=arr;                 
		    dg.validateNow();                      
		}
		
		private function httpHandle(e:ResultEvent):void
		{
			lblResult.text=e.result.sumTag;
		}
        ]]>  
  	</mx:Script>  
	<mx:HTTPService id="myHttp" showBusyCursor="true" result="httpHandle(event);" useProxy="false"/>
	<mx:Panel title="测试HTTPService" width="368" height="334" x="78" y="30" layout="absolute">
	    <mx:Label text="叠加参数：" x="110" y="26"/>
	    <mx:TextInput id="txtPara" x="161" y="24" width="95"/> 
	    <mx:DataGrid id="dg" x="76" y="64" height="166" width="179">
	        <mx:columns>
	            <mx:DataGridColumn dataField="para" headerText="参数列表"/>
	        </mx:columns>
	    </mx:DataGrid>
	    <mx:Button label="添加" click="addData();" x="277" y="26"/>  
	    <mx:Button label="删除" click="delData();" x="277" y="64"/>  
	    <mx:Label text="叠加结果是：" x="58" y="253"/>
	    <mx:Label x="126" y="253" id="lblResult"/>  
	    <mx:Button label="计算" click="addHandle();" x="277" y="249"/>   
	</mx:Panel>
</mx:Application>

public void doPost(HttpServletRequest request, HttpServletResponse response)
			throws ServletException, IOException
	{
		String[] para = request.getParameterValues("num");
		int sum = 0;
		if (para != null)
		{
			for (int i = 0; i < para.length; i++)
			{
				if (para[i] != null && !"".equals(para[i]))
				{
					sum = sum + Integer.parseInt(para[i]);
				}
			}
		}
		response.getWriter().print(
				"<?xml version=\"1.0\" encoding=\"utf-8\"?><sumTag>" + sum
						+ "</sumTag>");
	}

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# DO NOT EDIT THIS FILE - edit the master and reinstall.
# (/tmp/crontab.9205 installed on Mon Oct 15 10:44:32 2007)
# (Cron version -- $Id: crontab.c,v 2.13 1994/01/17 03:20:37 vixie Exp $)
1 * * * * /syn_time.sh

#!/bin/sh
#
# This script will be executed *after* all the other init scripts.
# You can put your own initialization stuff in here if you don't
# want to do the full Sys V style init stuff.

touch /var/lock/subsys/local
/sbin/service crond start

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<div style="width:225px;text-overflow:ellipsis;overflow: hidden;">
asdfl;kasjdf;kasjdflkasdjfsdfasdlkjfa;lsdkfjas;ldkjfls;kdf
asdfl;kasjdf;kasjdflkasdjfsdfasdlkjfa;lsdkfjas;ldkjfls;kdf
asdfl;kasjdf;kasjdflkasdjfsdfasdlkjfa;lsdkfjas;ldkjfls;kd
</div>

<ul id=txt style="line-height:18px;display: list-item; list-style-position: inside;list-style-type:upper-roman; margin-left:0px;"> 
<li>这是一个名字叫小强的列表项目。他难道不是很可爱吗？</li>
<li>这是一个名字叫小强的列表项目。他难道不是很可爱吗？</li>
<li>这是一个名字叫小强的列表项目。他难道不是很可爱吗？</li>
</ul>

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#============================================================================
# Configure Main Scheduler Properties  
#============================================================================

org.quartz.scheduler.instanceName = TestScheduler
org.quartz.scheduler.instanceId = AUTO

#============================================================================
# Configure ThreadPool  
#============================================================================

org.quartz.threadPool.class = org.quartz.simpl.SimpleThreadPool
org.quartz.threadPool.threadCount = 3
org.quartz.threadPool.threadPriority = 5

#============================================================================
# Configure JobStore  
#============================================================================

org.quartz.jobStore.misfireThreshold = 60000

org.quartz.jobStore.class = org.quartz.simpl.RAMJobStore

<servlet>
		<servlet-name>QuartzStartupServlet</servlet-name>
		<servlet-class>com.StartupServlet</servlet-class>
		<load-on-startup>1</load-on-startup>
</servlet>

public void init(ServletConfig cfg) throws javax.servlet.ServletException
	{
		log.info("server start!");
		initScheduler(cfg);
	}

	protected void initScheduler(ServletConfig cfg)
	{
		try
		{
			SchedulerFactory sf = new StdSchedulerFactory();
			Scheduler scheduler = sf.getScheduler();
			SimpleExample simpleExample = new SimpleExample(scheduler);
			simpleExample.run();
		} catch (Exception e)
		{
			log.error(e);
		}
	}

<servlet>
		<display-name>Quartz Initializer Servlet</display-name>
		<servlet-name>QuartzInitializer</servlet-name>
		<servlet-class>
			org.quartz.ee.servlet.QuartzInitializerServlet
		</servlet-class>
		<load-on-startup>1</load-on-startup>
	</servlet>

#============================================================================
# Configure Plugins 
#============================================================================

org.quartz.plugin.triggHistory.class = org.quartz.plugins.history.LoggingJobHistoryPlugin

org.quartz.plugin.jobInitializer.class = org.quartz.plugins.xml.JobInitializationPlugin
org.quartz.plugin.jobInitializer.fileNames = jobs.xml
org.quartz.plugin.jobInitializer.overWriteExistingJobs = true
org.quartz.plugin.jobInitializer.failOnFileNotFound = true
org.quartz.plugin.jobInitializer.scanInterval = 10
org.quartz.plugin.jobInitializer.wrapInUserTransaction = false

<?xml version="1.0" encoding="UTF-8"?>
<quartz>
	<job>
		<job-detail>
			<name>dailybalance</name>
			<group>balance</group>
			<job-class>com.SimpleJob</job-class>
		</job-detail>
		<trigger>
			<cron>
				<name>dailybalancetrigger</name>
				<group>balance</group>
				<job-name>dailybalance</job-name>
				<job-group>balance</job-group>
				<cron-expression>0/50 * * * * ?</cron-expression>
			</cron>
		</trigger>
	</job>
</quartz>

SchedulerFactory sf = new StdSchedulerFactory();
Scheduler scheduler = sf.getScheduler();
......
......
cfg.getServletContext().setAttribute(SCHEDULER_KEY, scheduler);
scheduler.start();

/**
	 * reset quartz job time
	 */
	public void reSetLotteryLoseBiddingTime(int lotteryType, Date date)
	{
		scheduler = (Scheduler) this.ctt.getAttribute(TIMING_WORK_SCHEDULER);
		try
		{
			String lotteryName = "";
			switch (lotteryType)
			{
			case 1://
				lotteryName = "ssq";
				break;
			case 2:// 
			{
				lotteryName = "3D";
			}
				break;
			case 3:// 
			{
				lotteryName = "qlc";
			}
				break;
			case 4:// 
			{
				lotteryName = "23x5";
			}
				break;
			default:
				break;
			}

			loseBiddingJob = scheduler.getJobDetail("loseBiddingJob_" + lotteryName, "loseBiddingGroup");
			loseBiddingTrigger = (CronTrigger) scheduler.getTrigger("loseBiddingTrigger_" + lotteryName,
					"loseBiddingGroup");
			String time = loseBiddingTrigger.getCronExpression();
			if (!time.equals(this.timeFormat(date)))
			{
				loseBiddingTrigger = new CronTrigger("loseBiddingTrigger_" + lotteryName, "loseBiddingGroup",
						"loseBiddingJob_" + lotteryName, "loseBiddingGroup", this.timeFormat(date));
				Date ft = scheduler.rescheduleJob("loseBiddingTrigger_" + lotteryName, "loseBiddingGroup",
						loseBiddingTrigger);
				log.info(loseBiddingJob.getFullName() + " has been scheduled to run at: " + ft
						+ " and excute based on expression: " + loseBiddingTrigger.getCronExpression());
			}
		} catch (Exception e)
		{
			log.error(e);
		}
	}

public boolean compareService(String thisIp)
{
	Properties properties = this.getProperties();
	String serviceIp = "";
	try
	{
		if (properties != null)
		{
			serviceIp = properties.getProperty("timing.work.serviceIp");
			if (serviceIp != null && serviceIp.equals(thisIp))
				return true;
		}
	} catch (Exception e)
	{
		log.error(e);
	}
	return false;
}

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/**
	 * 查询方案金额总和
	 * 
	 * @param lottype
	 *            Long
	 * @return double
	 */
	public Double getsummation(Long lottype)
	{
		double sum = 0.00;
		List ls = null;
		String sql = "";
		if (sql != null)
		{
			sql = " select sum(amount) from OccurProj a where a.lottype = '" + lottype + "' ";
		}
		ls = find(sql);
		if (ls != null && ls.isEmpty())
		{
			for (int i = 0; i < ls.size(); i++)
			{
				OccurProj occurproj = (OccurProj) ls.get(i);
				sum = sum + occurproj.getAmount();
			}
		}
		return sum;
	}

/**
	 * 根据主键查询明细信息
	 * 
	 * @param occurproj
	 *            OccurProj
	 * @return String
	 */
	public List queryById(Long id)
	{
		String sql = "";
		if (sql != null)
		{
			sql = " from OccurProj a where a.id=" + id;
		}
		return translateCode2Str(find(sql));
	}

/**
	 * 参数查询
	 * 
	 * @param occurproj
	 *            OccurProj
	 * @param lottype
	 *            Long
	 * @return List
	 */
	public List queryByPara(String username, Long lottype)
	{
		String sql = "";
		if (username != null && username.length() > 0)
		{
			sql += " and a.username like ? ";
		}
		if (sql.length() > 1)
		{
			sql = " from OccurProj a where 1=1 and a.lottype = '" + lottype + "' " + sql;
		} else
			sql = " from OccurProj where lottype = '" + lottype + "' ";
		return translateCode2Str(find(sql));
	}

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• JavaEye问答大赛开始了！ 从6月23日 至 7月6日，奖品丰厚 ！
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• JavaEye问答大赛开始了！ 从6月23日 至 7月6日，奖品丰厚 ！
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• 北京: 千橡集团暨校内网诚聘软件研发工程师
• Oracle专区上线，有Oracle最新文章，重要下载及知识库等精彩内容，欢迎访问。

• 快来参加7月17日在成都举行的SOA中国技术论坛
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