A COMPARATIVE STUDY OF TWO METHODOLOGIES FOR BINARY DATASETS ANALYSIS

Abstract: Studied are differences of two approaches targeted to reveal latent variables in binary data. These approaches assume that the observed high dimensional data are driven by a small number of hidden binary sources combined due to Boolean superposition. The first approach is the Boolean matrix factorization (BMF) and the second one is the Boolean factor analysis (BFA). The two BMF methods are used for comparison. First is the M8 method from the BMDP statistical software package and the second one is the method suggested by Belohlavek & Vychodil. These two are compared to BFA, especially with the Expectationmaximization Boolean Factor Analysis we had developed earlier has, however, been extended with a binarization step developed here. The well-known bars problem and the mushroom dataset are used for revealing the methods' peculiarities. In particular, the reconstruction ability of the computed factors and the information gain as the measure of dimension reduction was under scrutiny. It was shown that BFA slightly loses to BMF in performance when noise-free signals are analyzed. Conversely, BMF loses considerably to BFA when input signals are noisy

Cluster Analysis and Textual Data

Applicability of the cluster analysis in the area of a large textual databases is studied. Main principles of clustering algorithms are discussed and compared from the point of view of their applicability in this field

Slučování dat a evoluční optimalizace dotazu pro sofistikované vyhledávání

An innovative meta-search engine named WebFusion has been presented. The search system learns the expertness of every particular underlying search engine in a certain category based on the users' preferences according to an analysis of click-through behavior. In addition, an intelligent reranking method based on ordered weighted averaging (OWA) was introduced. The re-ranking method was used to fuse the results' scores of the underlying search engines. Independently, a progressive application of evolutionary computing to optimize Boolean search queries in crisp and fuzzy information retrieval systems was investigated, evaluated in laboratory environment and presented. In this paper we describe proposed incorporation of these two innovative recent methods founding an advanced Internet search application

Vybrané rozšířené příspěvky z mezinárodní konference DCCA 2007 (Digitální Komunikace a Počítačové Aplikace) - speciální číslo časopisu NNW

Editors present extended versions of selected papers from DCCA 2007 conference. This conference has been a forum for scientists and engineers to meet and to present their latest research results, ideas, and papers in the diverse areas of Digital Communications, Computer Science, and Information Technology. The selected papers are mainly from the area of artificial intelligence and applications, including biologically motivated methods. (Neural Network World 17, 4 (2007) 269-413.