Computation of term/document discrimination values by use of the cover coefficient concept

Abstract

Indexing in information retrieval (IR) is used to obtain a suitable vocabulary of index terms and optimum assignment of these terms to documents for increasing the effectiveness and efficiency of an IR system. The concept of term discrimination value (TDV) is one of the criteria used for index-term selection. In this article a new concept called the cover coefficient (CC) will be used in computing TDVs. After a brief introduction to the theory of indexing and the CC concept, an efficient way of computing TDVs by use of the CC concept, index-term selection , and weight modification are discussed. It is also shown that the computational cost of the CC approach in the calculation of TDVs is favorably comparable to the cost of a different approach that uses similarity coefficients. Furthermore, the TDVs obtained by the CC approach are consistent with those of the latter approach.

A text mining approach to assist the general public in the retrieval of legal documents

“…To obtain the top k legal term list, we employ the TDV (Term Discrimination Value) method proposed in Can and Ozkarahan (). An optimal index term is one that can distinguish between two different documents and relate two similar documents.…”

High‐speed rough clustering for very large document collections

“…For example, the means clustering algorithm (Kogan, 2001; Dhillon, Kogan, & Nicholas, 2004) and simultaneous keyword identification and clustering of text document (SKWIC; Frigui & Nasraoui, 2004) would be categorized into this type of clustering approach. Also, cover‐coefficient‐based concept clustering methodology (C 3 M; Can & Ozkarahan, 1984, 1985, 1987, 1990) is a k‐medoids method that automatically determines the number of clusters according to a criterion. These algorithms are desirable in that the number of clusters is reasonably determined based on a criterion, but inevitably, many computations are needed for optimizing the criteria.…”

Web Personalisation with the Cover Coefficient Algorithm

“…The CC technique works from one particular web user to calculate a probabilistic similarity measure with an entire web user collection including that particular user. The measure is the probability of randomly selecting a web document from one particular user, and from all the users containing that web document, the probability of randomly selecting a second particular user [10,3].…”

“…These groups do not always contain unique values; it is often found that values can overlap across many clusters [9]. In this work, our aim is to implement a clustering model based on the cover coefficient technique (CC) [10]. The clusters obtained from this process can be used to recommend pages to a new user of a web site.…”