Web Page Enrichment using a Rough Set Based Method

When documents are matched to a given query, often the terms in the query are matched to the words in the documents for calculating similarity. But it is a good idea if the given document is represented in an enriched manner with not only the actual words occurring in the document but also with the synonyms of the important words. This would definitely improve the recall of the system. With its ability to deal with vagueness and fuzziness, tolerance rough set seems to be promising tool to model relations between terms and documents. In many information retrieval problems, especially in text classification, determining the relation between term-term and term-document is essential. In this work, the application of TRSM to web page classification was evaluated to determine its effectiveness as a way to enrich a web page

Design and Implementation of a Web Usage Mining Model Based On Upgrowth and Preflxspan

Web Usage Mining (WUM) integrates the techniques of two popular research fields - Data Mining and the Internet. By analyzing the potential rules hidden in web logs, WUM helps personalize the delivery of web content and improve web design, customer satisfaction and user navigation through pre-fetching and caching. This paper introduces two prevalent data mining algorithms - FPgrowth and PrefixSpan into WUM and they are applied in a real business case. Maximum Forward Path (MFP) is also used in the web usage mining model during sequential pattern mining along with PrefixSpan so as to reduce the interference of false visit caused by browser cache and raise the accuracy of mining frequent traversal paths. Detailed analysis and application on the corresponding results are discussed

Conservative and aggressive rough SVR modeling

AbstractSupport vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use ϵ-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders