A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
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The idea of web mining is to apply the tools and techniques of data mining to the world wide web data to induce “interesting” consequences that can be used to improve various web applications. The goal of web mining is relatively simple: provide both computationally and cognitively efficient methods for improving the value of information to users of the WWW. The need for computational efficiency is well-recognized by the data mining community, which sprung from the database community concern for efficient manipulation of large datasets. The motivation for cognitive efficiency is more elusive but at least as important. We present our initial development of a framework for gathering, analyzing, and redeploying web data. Similar to conventional data mining, the general idea is that good use of web data first requires the careful selection of data (both usage and content data), the deployment of appropriate learning methods, and the evaluation of the results of applying the results of learning in a web application. While we use web abstraction to refer to any certain abstracted form of a particular web space (including web content, web structure, and web usage), our framework includes tools for building, using, and visualizing such web abstractions. Our development of this framework is itself an experiment, based on our belief that we need such a framework to assess the various combinations of data, learning, and application evaluation methods. We present an example of the deployment of our framework to navigation improvement. We focus on the idea of web usage mining and the application of simple learning methods to improve user navigation. The abstractions we develop are called Navigation Compression Models (NCMs), and we show a method for creating them, using them, and visualizing them to aid in their understanding. Also, we present a general class of methods for evaluating these intended navigation improvements, and describe the experiments that we have conducted as the basis for building insight into the navigation improvement problem and the general problem of web mining. We hope to incrementally elaborate our framework so that we can eventually gain insight into such questions as “How can mining goals be formulated to provide learning method evaluation criteria?” or “What are the trade-offs between intrusive data gathering and navigation improvement?” (Abstract shortened by UMI.)