Incremental Mining of Closed Frequent Subtrees

We study the problem of mining closed frequent subtrees from tree databases that are updated regularly over time. Closed frequent subtrees provide condensed and complete information for all frequent subtrees in the database. Although mining closed frequent subtrees is in general faster than mining all frequent subtrees, this is still a very time consuming process, and thus it is undesirable to mine from scratch when the change to the database is small. The set of previous mined closed subtrees should be reused as much as possible to compute new emerging subtrees. We propose, in this paper, a novel and efficient incremental mining algorithm for closed frequent labeled ordered trees. We adopt a divide-and-conquer strategy and apply different mining techniques in different parts of the mining process. The proposed algorithm requires no additional scan of the whole database while its memory usage is reasonable. Our experimental study on both synthetic and real-life datasets demonstrates the efficiency and scalability of our algorithm.

[1]  Hiroki Arimura,et al.  Optimized Substructure Discovery for Semi-structured Data , 2002, PKDD.

[2]  J W Ballard,et al.  Data on the web? , 1995, Science.

[3]  Yun Chi,et al.  CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees , 2004, PAKDD.

[4]  Ricard Gavaldà,et al.  Mining adaptively frequent closed unlabeled rooted trees in data streams , 2008, KDD.

[5]  Joost N. Kok,et al.  Efficient discovery of frequent unordered trees , 2003 .

[6]  Hiroki Arimura,et al.  Efficient Substructure Discovery from Large Semi-Structured Data , 2001, IEICE Trans. Inf. Syst..

[7]  Hiroki Arimura,et al.  Online algorithms for mining semi-structured data stream , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[8]  Minoru Kanehisa,et al.  Mining significant tree patterns in carbohydrate sugar chains , 2008, ECCB.

[9]  Mohammed J. Zaki.  Efficiently mining frequent trees in a forest , 2002, KDD.

[10]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[11]  Jiawei Han,et al.  IncSpan: incremental mining of sequential patterns in large database , 2004, KDD.

[12]  Petra Perner,et al.  Advances in Data Mining , 2002, Lecture Notes in Computer Science.

[13]  Alexandre Termier,et al.  Dryade: a new approach for discovering closed frequent trees in heterogeneous tree databases , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[14]  Peng Gao,et al.  A New Marketing Channel Management Strategy Based on Frequent Subtree Mining , 2007 .

[15]  Arbee L. P. Chen,et al.  Discovering Frequent Tree Patterns over Data Streams , 2006, SDM.

[16]  Yun Chi,et al.  Frequent Subtree Mining - An Overview , 2004, Fundam. Informaticae.