The Scent of a Newsgroup: Providing Personalized Access to Usenet Sites through Web Mining? Giuseppe Manco1, Riccardo Ortale2, and Andrea Tagarelli2 1 ICAR-CNR – Institute of Italian National Research Council Via Bucci 41c, 87036 Rende (CS), Italy e-mail:
[email protected] 2 DEIS, University of Calabria Via Bucci 41c, 87036 Rende (CS), Italy e-mail: {ortale, tagarelli}@si.deis.unical.it Abstract. We investigate Web mining techniques focusing on a specific application scenario: the pro- blem of providing personalized access to Usenet sites accessible through a Web server. We analyze the data available from a Web-based Usenet server, and describe how traditional techniques for pattern discovery on the Web can be adapted to solve the problem of restructuring the access to news articles. In our framework, a personalized access tailored to the needs of each single user, can be devised according to both the content and the structure of the available data, and the past usage experience over such data. 1 Introduction The wide exploitation of new techniques and systems for generating and storing data has made available a huge amount of information, which can be profitably used in the decision processes. Such volumes of data highlight the need for analysis methodologies and tools. The term “Knowledge Discovery in Databases” is usually devoted to the (iterative and interactive) process of extracting valuable patterns from the data by exploiting Data Mining algorithms. In general, data mining algorithms find hidden structures, tendencies, associations and correlations among data, and mark significant information. An example of data mining application involving huge volumes of data is the detection of behavioral models on the Web.