document: Generating semantic annotations for frequent patterns with context analysis 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 nextstep - 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 anannotation 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 dictionary-like 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 .
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