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- W4246822134 abstract "This chapter explains the specifics of Web mining and introduces a classification of basic Web mining subtasks. Web content mining uses Web page content as the data source for the mining process. Data infrastructure for recommender systems may be either explicit with different scales of rating and satisfaction or implicit: product purchased, book read, song heard, or Web-site content clicked. The chapter illustrates the possibilities of Web mining using HITS, LOGSOM, and path-traversal algorithms. Mining path-traversal patterns represents an additional class of data-mining techniques, which is showing great promise. The chapter describes query-independent ranking of Web pages and main characteristics of PageRank algorithm and formalizes a text-mining framework specifying the refining and distillation phases. Latent semantic analysis is a way of partitioning free text using a statistical model of word usage that is similar to eigenvector decomposition and factor analysis. The chapter outlines latent semantic indexing methodology." @default.
- W4246822134 created "2022-05-12" @default.
- W4246822134 date "2019-10-17" @default.
- W4246822134 modified "2023-09-24" @default.
- W4246822134 title "WEB MINING AND TEXT MINING" @default.
- W4246822134 doi "https://doi.org/10.1002/9781119516057.ch11" @default.
- W4246822134 hasPublicationYear "2019" @default.
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