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