Download e-book for kindle: Data Mining: Foundations and Intelligent Paradigms: Volume by Dawn E. Holmes, Lakhmi C Jain

By Dawn E. Holmes, Lakhmi C Jain

ISBN-10: 3642231659

ISBN-13: 9783642231650

There are many helpful books to be had on facts mining idea and purposes. even if, in compiling a quantity titled “DATA MINING: Foundations and clever Paradigms: quantity 1: Clustering, organization and category” we want to introduce many of the most up-to-date advancements to a vast viewers of either experts and non-specialists during this field.

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

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Let I be an itemset and SI = < s1 , . . , sn > be the support time sequence of I. Given a reference sequence R = < r1 , . . , rn >, an interest measure for the similarity-profiled temporal association pattern is defined as D(R, SI ) which is a Lp norm (p = 1, 2, . . , ∞) based dissimilarity distance between R and SI . An itemset I is called a similar itemset if D(R, SI ) ≤ θ where θ is a dissimilarity threshold. In Fig. 2. 3 Mining Algorithm Similarity-profiled temporal association mining presents challenges in computation.

Of the five categories listed above, algorithms for pattern discovery in large temporal databases, however, are of more recent origin and are mostly discussed in data mining literature. Temporal pattern mining deals with the discovery of temporal patterns of interest in temporal data, where the interest is determined by the domain and the application. The diversity of applications has led to the development of many temporal pattern models. The three popular frameworks of temporal pattern discovery are sequence mining(or frequent sequence pattern discovery), frequent episode discovery and temporal association rule discovery [33].

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 ∪ . . ∪ tn , where ti is a time slot by a time granularity, ti ∩ tj = ∅, i = j 3) A timestamped transaction database D=D1 ∪ . . ∪ Dn , Di ∩ Dj = ∅, i = j. Each transaction d ∈ D is a tuple < timestamp, itemset > where timestamp is a time ∈ T that the transaction is executed, and itemset ⊆ I.

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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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