Description: Clustering genetic algorithm to solve the problem. . While the weight and time are: 1- (user recently marked the time- the edge is marked time)* 0.01, the time interval in months, a difference of a month the weight difference of 0.01, recently marked the weight is 1 2 edge weight degree of adjustment by the end of the 3 co-occurrence, if the user does not label, then the weight of the largest part of its label from the label of the connecting structure (4) If the user label for less than the default size, it is repeated several times with the labels until equal to the number and the default size
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聚类推荐（传引用）\breast-cancer-wisconsin.data
..................\clusteringEvaluation.m
..................\disInnerClusterComputing.asv
..................\disInnerClusterComputing.m
..................\distanceComputing.m
..................\distanceComputing2.m
..................\dis_yeast.txt
..................\eig.prj
..................\findNameInClass.asv
..................\findNameInClass.m
..................\fMeasure.asv
..................\fMeasure.m
..................\fMeasureComputing.asv
..................\fMeasureComputing.m
..................\getClusters.m
..................\getKEigenVector.m
..................\HMDistanceComputing.m
..................\incrementalSearchClustering.asv
..................\incrementalSearchClustering.m
..................\initialKmeansClustering.asv
..................\initialKmeansClustering.m
..................\initialKmeansClustering2.m
..................\initialKmeansClustering3.m
..................\initialKmeansClustering4.m
..................\iris.data
..................\kmeansClustering.m
..................\makeInitCluster.asv
..................\makeInitCluster.m
..................\makeNewByReplaceOnlyOne.m
..................\makeNewCenter.m
..................\makeNewCenter2.m
..................\makeNewClusters.m
..................\makeRepalcePairs.m
..................\movement_libras.data
..................\nMedoidsClustering.m
..................\printDis.m
..................\printDis2File.m
..................\readBreastCancer.asv
..................\readBreastCancer.m
..................\readIris.m
..................\readIrise.asv
..................\readMovementInitialData.asv
..................\readMovementInitialData.m
..................\readSoybean.m
..................\readspanbase.m
..................\readTransfusion.asv
..................\readTransfusion.m
..................\readWDBCInitialData.asv
..................\readWDBCInitialData.m
..................\readyeast.asv
..................\readyeast.m
..................\readZoo.m
..................\reaWine.asv
..................\reaWine.m
..................\reaZoo.asv
..................\replaceProfitComputing.asv
..................\replaceProfitComputing.m
..................\r_train.txt
..................\spambase.data
..................\topusers1.txt
..................\topusers2.txt
..................\trainingset.txt
..................\transfusion.data
..................\t_train.txt
..................\Untitled2.m
..................\Untitledx.asv
..................\Untitledx.m
..................\u_count.txt
..................\u_train.txt
..................\wdbc.data
..................\wine.data
..................\yeast.data
..................\zoo.data
..................\算法实现要点.txt
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