Data Mining Patterns: New Methods and Applications by pascal Poncelet, Florent Masseglia, Maguelonne Teisseire

By pascal Poncelet, Florent Masseglia, Maguelonne Teisseire

Because the creation of the Apriori set of rules a decade in the past, the matter of mining styles is turning into a really energetic study region, and effective thoughts were greatly utilized to the issues both in or technology. at the moment, the information mining neighborhood is concentrating on new difficulties akin to: mining new different types of styles, mining styles lower than constraints, contemplating new types of advanced info, and real-world purposes of those recommendations.

Data Mining styles: New equipment and Applications presents an total view of the new strategies for mining, and in addition explores new forms of styles. This e-book bargains theoretical frameworks and provides demanding situations and their attainable ideas bearing on development extractions, emphasizing either learn innovations and real-world purposes. info Mining styles: New tools and purposes portrays examine functions in information versions, options and methodologies for mining styles, multi-relational and multidimensional development mining, fuzzy information mining, facts streaming, incremental mining, and plenty of different topics.

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Most algorithms attempt to push either type of constraints during the mining process hoping to reduce the search space in one direction: from subsets to supersets or from supersets to subsets. , 2002) pushes both types of constraints but at the expense of efficiency. Focusing solely on reducing the search space by pruning the lattice of itemsets is not necessarily a winning strategy. While pushing constraints early seems conceptually beneficial, in practice the testing of the constraints can add significant overhead.

The greedy algorithm shown below is used for discretizing an attribute B. It makes successive passes over the table and, at each pass it adds a new cut point chosen among the boundary points of πB,A. , Ql } replace every value in Qi by i for 0 ≤ i ≤ l. The while loop runs for as long as candidate boundary points exist, and it is possible to find a new cut point p such that the distance d ( A | BP* ) is less than the previous distance d ( A | BP* ). An experiment performed on a synthetic database shows that a substantial amount of time (about 78% of the total time) is spent on decreasing the distance by the last 1%.

An itemset X is said to be infrequent if its support s is smaller than a given minimum support threshold σ; X is said to be too frequent if its support s is greater than a given maximum support Σ; and X is said to be large or frequent if its support s is greater or equal than σ and less or equal than Σ. chapter organization This chapter starts by defining the main two types of constraints in section 2. Related work is illustrated in section 3. Our leap frequent mining algorithm COFI-Leap is explained in Section 4.

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