Web Mining with Relational Clustering

TL;DR: An encompassing, self-contained introduction to the foundations of the broad field of fuzzy clustering is presented, with special emphasis on the interpretation of the two most encountered types of gradual cluster assignments: the fuzzy and the possibilistic membership degrees.

TL;DR: The different aspects of Web mining, like clustering, association rule mining, navigation, personalization, Semantic Web, information retrieval, text and image mining are considered under the existing taxonomy.

TL;DR: Empirical results clearly show that the proposed Web usage-mining framework is efficient, and a novel approach called "intelligent-miner" (i-Miner) is presented, which optimize the concurrent architecture of a fuzzy clustering algorithm and a fuzzy inference system to analyze the Web site visitor trends.

TL;DR: In this article, the authors derived necessary conditions for any finite number of quanta and associated quantization intervals of an optimum finite quantization scheme to achieve minimum average quantization noise power.

TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.

TL;DR: This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear and provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.