TY - GEN
T1 - Generating semantic annotations for frequent patterns with context analysis
AU - Mei, Qiaozhu
AU - Xin, Dong
AU - Cheng, Hong
AU - Man, Jiawei
AU - Zhai, Chengxiang
PY - 2006
Y1 - 2006
N2 - As a fundamental data mining task, frequent pattern mining has widespread applications in many different domains. Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns, but little attention has been paid to the important next step - interpreting the discovered frequent patterns. Although some recent work has studied the compression and summarization of frequent patterns, the proposed techniques can only annotate a frequent pattern with non-semantical information (e.g. support), which provides only limited help for a user to understand the patterns. In this paper, we propose the novel problem of generating semantic annotations for frequent patterns. The goal is to annotate a frequent pattern with in-depth, concise, and structured information that can better Indicate the hidden meanings of the pattern. We propose a general approach to generate such an annotation for a frequent pattern by constructing its context model, selecting informative context indicators, and extracting representative transactions and semantically similar patterns. This general approach has potentially many applications such as generating a dictionarylike description for a pattern, finding synonym patterns, discovering semantic relations, and summarizing semantic classes of a set of frequent patterns. Experiments on different datasets show that our approach is effective in generating semantic pattern annotations.
AB - As a fundamental data mining task, frequent pattern mining has widespread applications in many different domains. Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns, but little attention has been paid to the important next step - interpreting the discovered frequent patterns. Although some recent work has studied the compression and summarization of frequent patterns, the proposed techniques can only annotate a frequent pattern with non-semantical information (e.g. support), which provides only limited help for a user to understand the patterns. In this paper, we propose the novel problem of generating semantic annotations for frequent patterns. The goal is to annotate a frequent pattern with in-depth, concise, and structured information that can better Indicate the hidden meanings of the pattern. We propose a general approach to generate such an annotation for a frequent pattern by constructing its context model, selecting informative context indicators, and extracting representative transactions and semantically similar patterns. This general approach has potentially many applications such as generating a dictionarylike description for a pattern, finding synonym patterns, discovering semantic relations, and summarizing semantic classes of a set of frequent patterns. Experiments on different datasets show that our approach is effective in generating semantic pattern annotations.
KW - Frequent pattern
KW - Pattern annotation
KW - Pattern context
KW - Pattern semantic analysis
UR - http://www.scopus.com/inward/record.url?scp=33749579895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749579895&partnerID=8YFLogxK
U2 - 10.1145/1150402.1150441
DO - 10.1145/1150402.1150441
M3 - Conference contribution
AN - SCOPUS:33749579895
SN - 1595933395
SN - 9781595933393
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 337
EP - 346
BT - KDD 2006
PB - Association for Computing Machinery
T2 - KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 20 August 2006 through 23 August 2006
ER -