Carpenter: finding closed patterns in long biological datasets. The growth of bioinformatics has resulted in datasets with new characteristics. These datasets typically contain a large number of columns and a small number of rows. For example, many gene expression datasets may contain 10,000-100,000 columns but only 100-1000 rows.Such datasets pose a great challenge for existing (closed) frequent pattern discovery algorithms, since they have an exponential dependence on the average row length. In this paper, we describe a new algorithm called CARPENTER that is specially designed to handle datasets having a large number of attributes and relatively small number of rows. Several experiments on real bioinformatics datasets show that CARPENTER is orders of magnitude better than previous closed pattern mining algorithms like CLOSET and CHARM.

References in zbMATH (referenced in 6 articles )

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  1. Vimieiro, Renato; Moscato, Pablo: Disclosed: an efficient depth-first, top-down algorithm for mining disjunctive closed itemsets in high-dimensional data (2014)
  2. Cerf, Loïc; Besson, Jérémy; Nguyen, Kim-Ngan T.; Boulicaut, Jean-François: Closed and noise-tolerant patterns in (n)-ary relations (2013)
  3. Liu, Hongyan; Wang, Xiaoyu; He, Jun; Han, Jiawei; Xin, Dong; Shao, Zheng: Top-down mining of frequent closed patterns from very high dimensional data (2009)
  4. Liu, Guimei; Li, Jinyan; Wong, Limsoon: A new concise representation of frequent itemsets using generators and a positive border (2008) ioport
  5. Han, Jiawei; Cheng, Hong; Xin, Dong; Yan, Xifeng: Frequent pattern mining: Current status and future directions (2007) ioport
  6. Wang, Jianyong; Karypis, George: On efficiently summarizing categorical databases (2005) ioport