Efficiently mining long patterns from databases

By Roberto J. Bayardo Jr.. 1998

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “Nevertheless, rule-based models have some drawbacks, such as exponential complexity [130], they need an a-priori”
Frequent pattern mining with uncertain data (2009, methodology): “Similar techniques can be used in order to extend setenumeration based methods [1, 4, 6].”
Frequent pattern mining: current status and future directions (2007, methodology): “Mining max-patterns was first studied by Bayardo (1998), where MaxMiner (Bayardo 1998), an Apriori-based, level-wise, breadth-first search method was proposed to findmax-itemset by performing superset frequency pruning and subset infrequency pruning for search space reduction.”
A survey of frequent subgraph mining algorithms (2012, methodology): “Enumeration DAGs have been used extensively in Association Rule Mining (Bayardo 1998; Agarwal et al. 2001); and subsequently, in a variety of ways, by many subtree mining algorithms (Asai et al. 2002; Nijssen & Kok 2003; Asai et al. 2003; Chi et al. 2004a; Chi et al. 2005).”
Computing iceberg concept lattices with T (2002, unknown): “algorithms Apriori [2] and Max-Miner [6] on strongly correlated data sets (and is comparable with those algorithms on weakly correlated data sets).”
Efficiently Using Prefix-trees in Mining Frequent Itemsets (2003, result): “These datasets should be representative, as recent research papers [2, 3, 4, 11, 10, 8, 9], use these or similar datasets.”
Effective detection of sophisticated online banking fraud on extremely imbalanced data (2012, background): “We observe the following reasons that show its limitations in discovering patterns in extremely imbalanced data: (1) MDB-LL border uses the Max-miner algorithm [7] to directly calculate the borders both in D f and D g no matter how small the minimum support is.”
Detecting Group Differences: Mining Contrast Sets (2001, methodology): “For example, if they were trying to nd all EPs in census data between Texas and Michigan with g g 1:2 they would have t o e n umerate with Max-Miner all itemsets with support greater 25 in Texas and 30 in Michigan, 40 Texas and 50 Michigan, etc.”
Association mining (2006, background): “…OCD Mannila et al. 1994 OCD Sampling Naive extension Toivonen 1996 Partition Sampling Negative border All-MFS Gunopulos et al. 1997 Depth Project MFS Random walks MaxMiner Bayardo 1998 Apriori MFS Upward Closure Principle Pincer Search Lin & Kedem 1998 Apriori MFS Bi-directional…”
Mining sequential patterns by pattern-growth: the PrefixSpan approach (2004, background): “Similar to mining closed and maximal frequent patterns in transaction databases [17], [3], which mines only the longest frequent patterns (in the case of max-pattern mining) or the longest one with the same support (in the case of closed-pattern mining), for sequential pattern mining, it is also des”
Mining frequent patterns with counting inference (2000, methodology): “Hence, using pattern counting inference, has to perform much fewer support counts than the Apriori and the Max-Miner algorithms.”
Detecting change in categorical data: mining contrast sets (1999, methodology): “This is similar to subset-infrequency pruning used by Apriori [2] and Max-Miner [3].”