Efficient algorithms for the mining of constrained frequent patterns from uncertain data

Mining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject of numerous studies since its introduction. Most of these studies find all the frequent patterns from collection of precise data, in which the items within each datum or transaction are definitely known and precise. However, there are many real-life situations in which the user is interested in only some tiny portions of these frequent patterns. Finding all frequent patterns would then be redundant and waste lots of computation. This calls for constrained mining, which aims to find only those frequent patterns that are interesting to the user. Moreover, there are also many real-life situations in which the data are uncertain. This calls for uncertain data mining. In this article, we propose algorithms to efficiently find constrained frequent patterns from collections of uncertain data.

[1]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[2]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[3]  R. Agarwal.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[4]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[5]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[6]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[7]  Laks V. S. Lakshmanan,et al.  Efficient mining of constrained correlated sets , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[8]  Laks V. S. Lakshmanan,et al.  The segment support map: scalable mining of frequent itemsets , 2000, SKDD.

[9]  Jian Pei,et al.  Mining frequent patterns by pattern-growth: methodology and implications , 2000, SKDD.

[10]  Bhavani M. Thuraisingham,et al.  Data mining, national security, privacy and civil liberties , 2002, SKDD.

[11]  James Bailey,et al.  Fast Algorithms for Mining Emerging Patterns , 2002, PKDD.

[12]  Laks V. S. Lakshmanan,et al.  Exploiting succinct constraints using FP-trees , 2002, SKDD.

[13]  Jian Pei,et al.  Constrained frequent pattern mining: a pattern-growth view , 2002, SKDD.

[14]  Laks V. S. Lakshmanan,et al.  Efficient dynamic mining of constrained frequent sets , 2003, TODS.

[15]  Carson Kai-Sang Leung,et al.  CanTree: a tree structure for efficient incremental mining of frequent patterns , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[16]  Yufei Tao,et al.  Probabilistic Spatial Queries on Existentially Uncertain Data , 2005, SSTD.

[17]  Arno J. Knobbe,et al.  Maximally informative k-itemsets and their efficient discovery , 2006, KDD '06.

[18]  Wilfred Ng,et al.  Mining quantitative correlated patterns using an information-theoretic approach , 2006, KDD '06.

[19]  Srinivasan Parthasarathy,et al.  Summarizing itemset patterns using probabilistic models , 2006, KDD '06.

[20]  Srinivasan Parthasarathy,et al.  Out-of-core frequent pattern mining on a commodity PC , 2006, KDD '06.

[21]  Rong Ge,et al.  Constraint-driven clustering , 2007, KDD '07.

[22]  Edward Hung,et al.  Mining Frequent Itemsets from Uncertain Data , 2007, PAKDD.

[23]  Carson Kai-Sang Leung,et al.  Efficient Mining of Frequent Patterns from Uncertain Data , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[24]  Wei Su,et al.  Permu-pattern: discovery of mutable permutation patterns with proximity constraint , 2008, KDD.

[25]  Chi-Yin Chow,et al.  Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[26]  Ben Kao,et al.  A Decremental Approach for Mining Frequent Itemsets from Uncertain Data , 2008, PAKDD.

[27]  Vipin Kumar,et al.  Quantitative evaluation of approximate frequent pattern mining algorithms , 2008, KDD.

[28]  Carson Kai-Sang Leung,et al.  A Tree-Based Approach for Frequent Pattern Mining from Uncertain Data , 2008, PAKDD.

[29]  Jean-François Boulicaut,et al.  Data Peeler: Contraint-Based Closed Pattern Mining in n-ary Relations , 2008, SDM.

[30]  Feifei Li,et al.  Finding frequent items in probabilistic data , 2008, SIGMOD Conference.

[31]  Hans-Peter Kriegel,et al.  Probabilistic frequent itemset mining in uncertain databases , 2009, KDD.

[32]  Charu C. Aggarwal,et al.  Frequent pattern mining with uncertain data , 2009, KDD.

[33]  Carson Kai-Sang Leung,et al.  Mining of Frequent Itemsets from Streams of Uncertain Data , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[34]  Carson Kai-Sang Leung.  Frequent Itemset Mining with Constraints , 2009, Encyclopedia of Database Systems.