Data Mining: Foundations and Intelligent Paradigms: Volume by Dawn E. Holmes, Lakhmi C Jain PDF

By Dawn E. Holmes, Lakhmi C Jain

ISBN-10: 3642231659

ISBN-13: 9783642231650

There are many priceless books on hand on information mining thought and purposes. despite the fact that, in compiling a quantity titled “DATA MINING: Foundations and clever Paradigms: quantity 1: Clustering, organization and type” we want to introduce many of the newest advancements to a vast viewers of either experts and non-specialists during this field.

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Extra resources for Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification

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L − 1) matrix multiplication operations in total. It is clear that Trandom walk is the dominant factor in the clustering process. We find in the experiments that the random walk distance computation takes 98% of the total clustering time in SA-Cluster. To reduce the number of matrix multiplication, full-rank approximation optimization techniques on matrix computation based on Matrix Neumann Series and SVD decomposition are designed to improve efficiency in calculating the random walk distance.

U (I, Dn ) > Fig. 3 shows the computation of the lower and upper bound support sequences of an itemset I = {A, B}. 2 Lower Bounding Distance A lower bounding distance concept is used to find itemsets whose support sequences could not possibly match with a reference sequence under a given threshold. If the lower bounding distance of an itemset does not satisfy the dissimilarity threshold, its true distance also does not satisfy the threshold. Thus the lower bounding distance can be used to prune the itemset without computing its true distance.

In summary, the incremental algorithm for calculating the new random walk distance matrix RN,A , given the original RA and the weight increments {Δω1 , . . , Δωm } iteratively computes the increments ΔPAk for k = 1, . . (5). Finally the new random walk distance matrix RN,A = RA + ΔRA is returned. The total runtime cost of the clustering process with Inc-Cluster can be expressed as Trandom walk + (t − 1) · Tinc + t · (Tcentroid update + Tassignment ) where Tinc is the time for incremental computation and Trandom walk is the time for computing the random walk distance matrix at the beginning of clustering.

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Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification by Dawn E. Holmes, Lakhmi C Jain

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