Frequent Pattern Mining by Charu C. Aggarwal, Jiawei Han (eds.)

By Charu C. Aggarwal, Jiawei Han (eds.)

This accomplished reference includes 18 chapters from in demand researchers within the box. every one bankruptcy is self-contained, and synthesizes one element of widespread trend mining. An emphasis is put on simplifying the content material, in order that scholars and practitioners can enjoy the booklet. every one bankruptcy includes a survey describing key learn at the subject, a case learn and destiny instructions. Key themes contain: trend progress tools, widespread trend Mining in information Streams, Mining Graph styles, significant information widespread development Mining, Algorithms for facts Clustering and extra. Advanced-level scholars in computing device technological know-how, researchers and practitioners from will locate this booklet a useful reference.

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S. Verykios, A. K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni. Association rule hiding. IEEE Transactions on Knowledge and Data Engineering, pp. 434–447, 16(4), pp. 434– 447, 2004. 66. J. Vreeken, M. van Leeuwen, and A. Siebes. Krimp: Mining itemsets that compress. Data Mining and Knowledge Discovery, 23(1), pp. 169–214, 2011. 67. J. Wang, J. Han, and J. Pei. CLOSET+: Searching for the Best strategies for mining frequent closed itemsets. ACM KDD Conference, 2003. 68. Z. Xing, J. Pei, and E.

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This chapter will also focus on the first phase of frequent pattern mining, which is generally considered more important and non-trivial. Frequent patterns satisfy a downward closure property, according to which every subset of a frequent pattern is also frequent. This is because if a pattern P is a subset of a transaction, then every pattern P ⊆ P will also be a subset of T . Therefore, the support of P can be no less than that of P . The space of exploration of frequent patterns can be arranged as a lattice, in which every node is one of the 2d possible itemsets, and an edge represents an immediate subset relationship between these itemsets.

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