Mining frequent itemsets with convertible constraints

By Jian Pei, Jiawei Han, Laks V. S. Lakshmanan. Proceedings / International Conference on Data Engineering, 2001

391 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e563de7cb164dcfc624b1aa0373d2698f3e40ab5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jiawei Han
2. Jianyong Wang
3. Cynthia Rudin
4. Hong Cheng
5. Xifeng Yan
6. Siegfried Nijssen
7. Dong Xin
8. Élisa Fromont

In Information Systems

1. Jiawei Han
2. Hong Cheng
3. Xifeng Yan
4. Jianyong Wang
5. Cynthia Rudin
6. Dong Xin
7. Siegfried Nijssen
8. Jian Pei

In Signal Processing

1. Jiawei Han
2. Hong Cheng
3. Xifeng Yan
4. Dong Xin
5. Jianyong Wang
6. John F. Roddick
7. Siegfried Nijssen
8. Jian Pei

Citations in the corpus

• Association mining (2006, background): “…Reduced memory usage Adaptive algorithm behaviour Reduced Storage & memory management FP-Growth Han & Pe 2000 HL Hyper 2 Trie based H-Mine Pei et al. 2001 HL Hyper 2 Array based & partitioning TD-FP-Growth Wang et al. 2002 HL Hyper 2 Top down processing ITL-Mine Gopalan…”
• Mining top-k frequent closed patterns without minimum support (2002, background): “Constraint-based mining [4, 6 ] is essential to top-k mining since users may always want to put constraints on the data and rules to be mined.”
• Optimal constraint-based decision tree induction from itemset lattices (2010, background): “…other types of constraints than support; several categories of constraints were identified, among which monotonic, anti-monotonic , and convertible constraints, and algorithms were introduced to mine itemsets under these constraints (Pei et al. 2001; Bucila et al. 2003; Bonchi and Lucchese 2007).”
• A Survey of Sequential Pattern Mining (2017, background): “Pei et al. [91] and other researchers studied the characteristics of constraints that can be pushed deep into the process of mining sequential patterns, and other types of patterns [87, 88, 10].”
• Constraint programming for itemset mining (2008, neutral): “For quite some time, the data mining community has been interested in constraint-based mining, that is, the use of constraints to specify the desired properties of the patterns to be mined [1, 4, 5, 6, 8, 11, 12, 15, 16, 10].”
• Frequent pattern mining: current status and future directions (2007, methodology): “…alternatives and extensions to the FP-growth approach, including depth-first generation of frequent itemsets by Agarwal et al. (2001); H-Mine , by Pei et al. (2001) which explores a hyper-structure mining of frequent patterns; building alternative trees; exploring top-down and bottom-up…”
• Mining sequential patterns by pattern-growth: the PrefixSpan approach (2004, background): “In general, constraints can be characterized based on the notion of monotonicity, antimonotonicity, succinctness, as well as convertible and inconvertible constraints, respectively, depending on whether a constraint can be transformed into one of these categories if it does not naturally belong to o”
• H-mine: hyper-structure mining of frequent patterns in large databases (2001, background): “Its structure- and space-preserving mining methodology may have strong impact on the development of new, efficient and scalable data mining methods for mining other kinds of patterns, such as closed-itemsets [11], max-patterns [4], sequential patterns [14, 12], constraintbased mining [9, 10 ], etc.”