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information retrieval Context indicator w_bruce_croft; web information; monika_rauch_henzinger;

james_p_callan; full-text

Representative transactions web information retrieval

Representative transactions language model information retrieval

Semantic similar patterns information use; web information; probabilistic information; information filter;

text information

Consider two types of patterns: (1) frequent author or coauthorship, each of which is a frequent itemset of authors, and (2) frequent title terms, each of which is a frequent sequential pattern of the title words. The method can automatically generate dictionary-like annotations for different kinds of frequent patterns. For frequent itemsets like coauthorship or single authors, the strongest context indicators are usually the other coauthors and discriminative title terms that appear in their work. The semantically similar patterns extracted also reflect the authors and terms related to their work. However, these similar patterns may not even co-occur with the given pattern in a paper. For example, the patterns “timos_k_selli,” “ramakrishnan_srikant,” and so on, do not co-occur with the pattern “christos_faloutsos,” but are extracted because their contexts are similar since they all are database and/or data mining researchers; thus the annotation is meaningful.

For the title term “information retrieval,” which is a sequential pattern, its strongest context indicators are usually the authors who tend to use the term in the titles of their papers, or the terms that tend to coappear with it. Its semantically similar patterns usually provide interesting concepts or descriptive terms, which are close in meaning (e.g., “information retrieval → information filter).”

In both scenarios, the representative transactions extracted give us the titles of papers that effectively capture the meaning of the given patterns. The experiment demonstrates the effectiveness of semantic pattern annotation to generate a dictionary-like annotation for frequent patterns, which can help a user understand the meaning of annotated patterns.

The context modeling and semantic analysis method presented here is general and can deal with any type of frequent patterns with context information. Such semantic annotations can have many other applications such as ranking patterns, categorizing and clustering patterns with semantics, and summarizing databases. Applications of the pattern context model and semantical analysis method are also not limited to pattern annotation; other example applications include pattern compression, transaction clustering, pattern relations discovery, and pattern synonym discovery.

7.6.2. Applications of Pattern Mining

We have studied many aspects of frequent pattern mining, with topics ranging from efficient mining algorithms and the diversity of patterns to pattern interestingness, pattern compression/approximation, and semantic pattern annotation. Let's take a moment to consider why this field has generated so much attention. What are some of the application areas in which frequent pattern mining is useful? This section presents an overview of applications for frequent pattern mining. We have touched on several application areas already, such as market basket analysis and correlation analysis, yet frequent pattern mining can be applied to many other areas as well. These range from data preprocessing and classification to clustering and the analysis of complex data.

To summarize, frequent pattern mining is a data mining task that discovers patterns that occur frequently together and/or have some distinctive properties that distinguish them from others, often disclosing something inherent and valuable. The patterns may be itemsets, subsequences, substructures, or values. The task also includes the discovery of rare patterns, revealing items that occur very rarely together yet are of interest. Uncovering frequent patterns and rare patterns leads to many broad and interesting applications, described as follows.

Pattern mining is widely used for noise filtering