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A methodology for dynamic data mining based on fuzzy clustering Source: Fuzzy Sets and Systems Volume: 150, Issue: 2, March 1, 2005, pp. 267-284 Authors: Fernando Crespo、Richard Weber Speaker: 黃琬淑(Wan-Shu Huang) Date: 2005/12/22 1 Outline Introduction Dynamic data mining using fuzzy clustering Application Conclusions and comment 2 Introduction(1/3) Clustering technique is to group similar objects into the same classes Keep applying data mining system in a changing environment 1.Neglects changes and without any updating 2.A new system is developed 3.Update of the classifier 3 Introduction(2/3) Propose a methodology follow strategy 3 First identify the need for a system’s update by applying it to new data. Second perform the update by using efficiently the previous system. 4 Introduction(3/3) Hierarchical clustering e.g. CHAMELEON Partitional clustering e.g. c-means and fuzzy c-means Taxonomy of dynamic data mining for clustering 5 Dynamic data mining using fuzzy clustering(1/11) Possible changes of the classifier’s structure Creation of new classes Elimination of classes Movement of classes 6 Dynamic data mining using fuzzy clustering(2/11) 7 Dynamic data mining using fuzzy clustering(3/11) Step 1 Identify objects that represent changes d (vi , v j ) i j , dˆik dˆ ( xk , vi ), i, j 1,..., c. i 1,..., c, k n 1,..., n m. uˆik , i 1,..., c, k n 1,..., n m. 8 Dynamic data mining using fuzzy clustering(4/11) Condition 1:not classified well by the existing classifier 1 uˆ ik k n 1,..., n m i 1,..., c. c Condition 2:far away from the current classes 1 ˆ d i k min d (vi , v j ) k n 1,..., n m i j 1,..., c. 2 9 Dynamic data mining using fuzzy clustering(5/11) Based on these two conditions 1 x k fulfills Conditions 1 and 2, 1IC ( x k ) 0 else. If n m 1 (x ) 0 k n1 IC k , process with step 3.1 else go to step 2 10 Dynamic data mining using fuzzy clustering(6/11) Step2 Determine changes of class structure nm k n 1 1IC ( xk ) m with a parameter , 0 1. Above β create new classes (step 3.2) else just move the existing classes (step3.1) 11 Dynamic data mining using fuzzy clustering(7/11) Step3.1 Move classes 1 object k is assigned to class i, 1Ci ( xk ) 0 else. ˆvi (1 i )vi i vi , i n m (1Ci ( xk ) (1 1IC ( xk )) uˆik ) k n1 . n n m (1Ci ( x j ) uij ) k n1 (1Ci ( xk ) (1 1IC ( xk )) uˆik ) j 1 12 Dynamic data mining using fuzzy clustering(8/11) Step 3.2 Create classes N c L(c) uik d ik2 , k 1 i 1 S (c) structure strength (effectiveness of classifica tion) (1 )( accuracy of classifica tion) log( N / c) (1 ) log( L(1) / L(c)). C 越大,E越小,L(c)越小,L越大 C 越小,E越大,L(c)越大,L越小 13 Dynamic data mining using fuzzy clustering(9/11) 14 Dynamic data mining using fuzzy clustering(10/11) Step 4 Identify trajectories of classes t c First set counter is i t Created class i in cycle t-1,set counter: ci =1 Class I is the result of moving a class j in cycle t-1, set counter: c t c t 1 1 i j 15 Dynamic data mining using fuzzy clustering(11/11) Step 5 Eliminate unchanged classes A class has to be eliminated if it did not receive new objects for a long period. 16 Application of the proposed methodology(1/8) 500 objects for each of the four classes Shows the initial data set (0,15)(8,35) (15,0)(15,20) 17 Application of the proposed methodology(2/8) Apply fuzzy c-means with c=4 and m=2 Presents the respective cluster solution 18 Application of the proposed methodology(3/8) In the first cycle 600 new objects arrive 19 Application of the proposed methodology(4/8) Results after fist cycle 20 Application of the proposed methodology(5/8) In the second cycle 500 new objects arrive 21 Application of the proposed methodology(6/8) Results after second cycle 22 Application of the proposed methodology(7/8) In the third cycle 600 new objects arrive 23 Application of the proposed methodology (8/8) Results after third cycle 24 Conclusions and comment Presented a methodology Used fuzzy c-means Provide updated class structures Analyzing changes in application domain The parameters of set is a question 25