Search

Document Clustering aims at organizing a large quantity of unlabeled documents into a smaller number of meaningful and coherent clusters. One of the main unsolved problems in the literature is the lack of a reliable methodology to evaluate the results, although a wide variety of validation measures has been proposed. Validation measures are often unsatisfactory with numerical data, and even underperforming with textual data. Our attention focuses on the use of cosine similarity into the clustering process. A new measure based on the same criterion is here proposed. The effectiveness of the proposal is shown by an extensive comparative study. [ABSTRACT FROM AUTHOR]

The article presents information on the Research Committee on Logic and Methodology (RC33) of the International Sociological Association (ISA). It is announced that the newsletter of the organization will appear in this publication beginning in this issue. Other periodicals and books created by RC33 which will be published by the publisher Sage Publications Ltd. are discussed. A summary of the committee's finances and a report on membership are given. A eulogy is offered for sociologist Hans van der Zouwen on the occasion of his 70th birthday and decision to retire from teaching and research.

The aim of this paper is to propose an extension of principal component analysis onto a reference subspace (PCAR) to the case where the same dependent variables have been measured on the same statistical units under two, or more, different observational conditions. As the units belong to the same multidimensional space, we profitably apply the orthogonal Procrustean rotations, jointly with PCAR, so as to enrich the interpretability of patterns on factorial planes. The proposed technique is applied to a problem of agreement in the area of sensory data analysis for representing evaluation gaps between the perception of quality by wine experts and ordinary consumers. The proposed approach allows to explain the eventually detected gaps in terms of the physical–chemical characteristics of wines. Copyright © 1999 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The aim of this paper is to give an overview of the methodological contribution given by Italian researchers in introducing a priori information into multidimensional data analysis techniques, paying special attention to categorical variables. The basic method is Non-Symmetrical Correspondence Analysis, which enables the analysis of a contingency table when the behaviour of one variable is supposed to be dependent on the other cross-classified variable. As usual correspondence analysis decomposes an association index (Pearson's Ω2), in a principal component sense, the proposed method is based on a decomposition of a predictability index (Goodman and Kruskal's τb). Non-symmetrical correspondence analysis has been extended to more than one dependent/explanatory variable(s), by means of proper flattening procedures, i.e. by the use of multiple tables, and the decomposition of Gray and Williams' multiple and partial τb's. In doing so multiple and partial versions have been proposed. A forward selection procedure for choosing the variables with higher predictive power is presented. After a brief review of non-symmetrical correspondence analysis confirmatory approach, the problem of validating results in terms of analytical stability and replication stability is faced by means of influence functions and resampling techniques. [ABSTRACT FROM AUTHOR]