Operations Research

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

By Dawn E. Holmes, Lakhmi C Jain

There are many precious books to be had on information mining concept and purposes. notwithstanding, in compiling a quantity titled “DATA MINING: Foundations and clever Paradigms: quantity 1: Clustering, organization and class” we want to introduce many of the most up-to-date advancements to a large viewers of either experts and non-specialists during this field.

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Efficient aggregation for graph summarization. In: Proc. 2008 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 2008), Vancouver, Canada, pp. 567–580 (June 2008) 14. : Graph clustering based on structural/attribute similarities. In: Proc. 2009 Int. Conf. on Very Large Data Base (VLDB 2009), Lyon, France (August 2009) 15. : Clustering large attributed graphs: An efficient incremental approach. In: Proc. 2010 Int. Conf. on Data Mining (ICDM 2010), Sydney, Australia (December 2010) 16. : Graphscope: parameter-free mining of large time-evolving graphs.

2 shows a simple illustration of the pattern mining. Temporal Data Mining: Similarity-Profiled Association Pattern 33 Input Time−stamped transactions time . t1 t1 t1 t1 t1 t1 t1 t1 t1 t1 items A A, B, C A, C A A, B, C C C A, B, C C C time t2 t2 t2 t2 t2 t2 t2 t2 t2 t2 items B, C B A, B, C A, B, C C A, B, C A, C C B B, C . 0 support(t0) . 14) (b) (c) Fig. 2. An example of similarity-profiled temporal association mining (a) Input data (b) Generated support time sequences, and sequence search (c) Output itemsets Given 1) A finite set of items I 2) An interest time period T =t1 ∪ .

Ln >=< L(I, D1 ), . . , L(I, Dn ) > UI =< u1 , . . , un >=< U (I, D1 ), . . , 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.

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