Scalable Algorithms for Association Mining

By Mohammed J. Zaki. 2000

1,905 citations recorded by the source corpus.

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

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Citations in the corpus

MurTree: Optimal Decision Trees via Dynamic Programming and Search (2020, methodology): “Ordering search nodes according to a heuristic is common in combinatorial optimisation, e.g., variable selection in integer/constraint programming, and in the data mining literature, e.g., Zaki (2000); Zimmermann and De Raedt (2009).”
DATA MINING TECHNIQUES (2003, methodology): “One of the first efficient approach was the Apriori algorithm [3], which inspired the development of many other efficient algorithms[14, 35, 50, 58, 71, 73, 81].”
Forecasting: theory and practice (2020, neutral): “Thus, several algorithms have been proposed to solve the problem of frequent itemset extraction (Zaki, 2000), some specifically addressing high-dimensionality issues (Apiletti et al., 2017, 2015).”
AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022, methodology): “Common association rule learning techniques such as AIS [2], Apriori [3], FP-Tree [37], RARM [18], Eclat [105], ABC-RuleMiner [88], and others can be used to build a rule-based model utilizing a given data set.”
Introduction to arules — Mining Association Rules and Frequent Item Sets (2006, methodology): “This format is often called the horizontal database layout (Zaki, 2000).”
Profiling relational data: a survey (2015, methodology): “Popular algorithms for efﬁciently discovering frequent patterns include Apriori [8], Eclat [141], and FP-Growth [67].”
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases (2013, methodology): “The framework of the proposed methods consists of three steps: 1) Scan the database twice to construct a global UPTree with the first two strategies (given in Section 3.1); 2) recursively generate PHUIs from global UP-Tree and local UP-Trees by UP-Growth with the third and fourth strategies (given…”
Mining high utility itemsets without candidate generation (2012, methodology): “1, the supports of { a } , { ab } , { abc } , and { abcd } are 4, 3, 2, and 1, but the utilities of these itemsets are 16, 26, 21, and 14, respectively.”
A taxonomy of sequential pattern mining algorithms (2010, methodology): “, GSP [Srikant and Agrawal 1996], ECLAT [Zaki 2000]).”
Efficiently mining frequent trees in a forest (2002, methodology): “is called a prefix-based equivalence relation [ 12 ].”
Fast vertical mining using diffsets (2003, methodology): “Since the diffsets are a small fraction of the size of tidsets, intersection operations are performed extremely fast! We show how diffsets improve by several orders of magnitude the running time of vertical algorithms like Eclat [22] that mines all frequent itemsets.”
Frequent pattern mining: current status and future directions (2007, methodology): “Zaki (2000) proposed Equivalence CLASS Transformation (Eclat) algorithm by exploring the vertical data format.”
Fast algorithms for frequent itemset mining using FP-trees (2005, background): “Therefore, some existing algorithms only mine maximal frequent itemsets.”
Association mining (2006, methodology): “Summary of Condensed Representation and Incomplete Algorithms Name Author Year Base Principle Contribution Condensed Representation Algorithms Close Pasquier et al. 1999 Apriori Closed Sets Gauloise Connection A-Close Pasquier et al. 1999 Close Closed Set CLOSET Pei et al. 2000 FP-Growth Closed Se”