Contrast data mining : concepts, algorithms, and by Guozhu Dong, James Bailey

By Guozhu Dong, James Bailey

''Preface Contrasting is among the most simple kinds of research. Contrasting dependent research is mostly hired, usually subconsciously, through all kinds of individuals. humans use contrasting to raised comprehend the area round them and the demanding difficulties they wish to unravel. humans use contrasting to appropriately check the desirability of significant events, and to assist them greater keep away from almost certainly harmful Read more...

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Background on Binary Decision Diagrams and ZBDDs . . . . . Mining Emerging Patterns Using ZBDDs . . . . . . . . . . . . Discussion and Summary . . . . . . . . . . . . . . . . . . . . 1 Introduction 31 32 35 38 In this chapter, we study the computation of emerging patterns using a sophisticated data structure, known as a zero-suppressed binary decision diagram (ZBDD). We will see how the ZBDD data structure can be used to enumerate emerging patterns.

4 13 14 15 18 19 20 An important task when working with contrast patterns is the assessment of their quality or discriminative ability. In this chapter, we review a range of measures that may be used to assess the discriminative ability of contrast patterns. Some of these measures have their origins in association rules, others in statistics, and others in subgroup discovery. Our presentation is not exhaustive, since dozens of measures exist. Instead we present a selection that covers a number of the main types.

An itemset X is a closed itemset if for every itemset Y such that X ⊂ Y , support(Y, D) < support(X, D). X is a (minimal) generator if for every itemset Z such that Z ⊂ X, support(Z, D) > support(X, D). Using these concepts, one may form equivalence classes from a dataset D, corresponding to sets of transactions. For each equivalence class, there is exactly one closed pattern and one or more generators. Both the closed pattern and the generators are contained in all transactions in their equivalence class.

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