Frequent pattern mining: current status and future directions

By Jiawei Han, Hong Cheng, Dong Xin, Xifeng Yan. 2007

1,544 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study (2009, methodology): “To overcome this difficulty, in this section we present a technique based on frequent pattern mining ([3], [15] and a survey [5]) through an efficient pattern identification process over the spectrum data, which generates predictions of future channel state based on a collection of past observations”
DCUBE: discrimination discovery in databases (2010, background): “Data from the input table (1) is fetched and frequent itemsets [3] are extracted for the specific minimum support (2).”
Data-driven Automatic Treatment Regimen Development and Recommendation (2016, background): “In general sense, frequent pattern mining [4] is also related with our work, but it is not applicable in this study because the treatment records vary in many dimensions, leading to no frequent patterns with signiﬁcant pattern support.”
Mining Individual Life Pattern Based on Location History (2009, background): “In [19], a hierarchicalgraph-based similarity measurement is developed to mine similarity between different users from location history.”
Parameter-free probabilistic API mining across GitHub (2015, methodology): “First, frequent sequence mining methods depend on a user-specified dataset-specific minimum frequency threshold [5, 17].”
2015 Ieee International Conference on Big Data (big Data) Task-based Recommendation on a Web-scale (positive): “The extraction of users' demand sequence patterns requires Frequent Pattern Mining (FPM) techniques [20], [21] , which is a key research topic in the community of data ming.”
ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification (2012, methodology): “…Apriori method by Agrawal and Srikant [1994], researchers have proposed many algorithms for frequent pattern mining that apply heuristic techniques to traverse the search space, which grows exponentially with the number of items in the dataset [Han et al., 2007, Hipp et al., 2000, Goethals, 2003].”
Parameter-Free Probabilistic API Mining at GitHub Scale (2015, methodology): “First, frequent sequence mining methods depend on a user-specified dataset-specific minimum frequency threshold [4, 15].”
MacroBase: Analytic Monitoring for the Internet of Things (2016, methodology): “We also compared to a data cubing strategy suggested by Roy and Suciu [42], which generates counts for all possible combinations (21x slower), Apriori itemset mining [22] (over 43x slower), and Data X-Ray [46].”
A survey of frequent subgraph mining algorithms (2012, background): “A more recent review of mining frequent patterns including: itemsets, subsequence, and subgraphs; appears in Han et al. (2007).”
Data Mining for Internet of Things: A Survey (2014, background): “One possible way to make a system smart, or “think” like a human, is using data mining technologies, but how to make it work as expected is still a difficult problem today, just like we have gone a long way trying to make computers think by themselves.”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, methodology): “Abundant literature can be found presenting a tremendous progress in the topic [51, 98].”
Exploring discriminative pose sub-patterns for effective action classification (2013, methodology): “In this work, we use Apriori algorithm [2, 18] to mine the frequent items from quantized human pose data, namely, the BQ10 binary string introduced in Section 5.”
MacroBase: Prioritizing Attention in Fast Data (2016, methodology): “The literature contains several recent explanation techniques leveraging decision-tree [23] and Apriori-like [39, 80] pruning, grid search [68, 82], data cubing [69], Bayesian statistics [78], visualization [17,62], causal reasoning [81], and several others [31, 43, 51, 58].”
Mining Frequent Patterns with Differential Privacy (2013, background): “Frequent patterns have drawn the attention of the data mining community over several decades leading to the development of many algorithmic techniques, see [11] for a survey of results.”
Mining high utility itemsets without candidate generation (2012, background): “Among the topics, the high utility itemset mining problem is one of the most important, and it derives from the famous frequent itemset mining problem [7, 8].”