Fundamenta Informaticae - Volume 127, issue 1-4

Authors: Kryszkiewicz, Marzena

Article Type: Research Article

Abstract: The cosine and Tanimoto similarity measures are typically applied in the area of chemical informatics, bio-informatics, information retrieval, text and web mining as well as in very large databases for searching sufficiently similar vectors. In the case of large sparse high dimensional data sets such as text or Web data sets, one typically applies inverted indices for identification of candidates for sufficiently similar vectors to a given vector. In this article, we offer new theoretical results on how the knowledge about non-zero dimensions of real valued vectors can be used to reduce the number of candidates for vectors sufficiently cosine …and Tanimoto similar to a given one. We illustrate and discuss the usefulness of our findings on a sample collection of documents represented by a set of a few thousand real valued vectors with more than ten thousand dimensions. Show more

Keywords: sparse data sets, high dimensional data sets, the cosine similarity, the Tanimoto similarity, text mining, data mining, information retrieval, similarity joins, inverted indices

DOI: 10.3233/FI-2013-911

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 307-323, 2013

Authors: Kostek, Bozena | Kaczmarek, Andrzej

Article Type: Research Article

Abstract: This study aims to create an algorithm for assessing the degree to which songs belong to genres defined a priori. Such an algorithm is not aimed at providing unambiguous classification-labelling of songs, but at producing a multidimensional description encompassing all of the defined genres. The algorithm utilized data derived from the most relevant examples belonging to a particular genre of music. For this condition to be met, data must be appropriately selected. It is based on the fuzzy logic principles, which will be addressed further. The paper describes all steps of experiments along with examples of analyses and results obtained.

Keywords: Music Information Retrieval (MIR), Music genre classification, Music parametrization, Query systems, Intelligent decision systems

DOI: 10.3233/FI-2013-912

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 325-340, 2013

Authors: Wakulicz-Deja, Alicja | Nowak-Brzezińska, Agnieszka | Przybyła-Kasperek, Małgorzata

Article Type: Research Article

Abstract: This paper discusses the issues related to the conflict analysis method and the rough set theory, process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The value of the rough set theory and conflict analysis applied in practical decision support systems with complex domain knowledge are expressed. The furthermore examples of decision support systems with complex domain knowledge are presented in this article. The paper proposes a new approach to the organizational structure of a multi-agent decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the local …knowledge bases will be combined into groups in a dynamic way. We will seek to designate groups of local bases on which the test object is classified to the decision classes in a similar manner. Then, a process of knowledge inconsistencies elimination will be implemented for created groups. Global decisions will be made using one of the methods for analysis of conflicts. Show more

Keywords: knowledge bases, rough set theory, conflict analysis, decision support systems, cluster analysis, relation of friendship, relation of conflict

DOI: 10.3233/FI-2013-913

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 341-356, 2013

Authors: Peters, James F. | Ramanna, Sheela

Article Type: Research Article

Abstract: This paper introduces descriptive set patterns that originated from our visits with Zdzisław Pawlak and Andrzej Skowron at Banacha and environs in Warsaw. This paper also celebrates the generosity and caring manner of Andrzej Skowron, who made our visits to Warsaw memorable events. The inspiration for the recent discovery of descriptive set patterns can be traced back to our meetings at Banacha. Descriptive set patterns are collections of near sets that arise rather naturally in the context of an extension of Solomon Leader's uniform topology, which serves as a base topology for compact Hausdorff spaces that are proximity spaces. The …particular form of proximity space (called EF-proximity) reported here is an extension of the proximity space introduced by V. Efremovič during the first half of the 1930s. Proximally continuous functions introduced by Yu.V. Smirnov in 1952 lead to pattern generation of comparable set patterns. Set patterns themselves were first considered by T. Pavlidis in 1968 and led to U. Grenander's introduction of pattern generators during the 1990s. This article considers descriptive set patterns in EF-proximity spaces and their application in digital image classification. Images belong to the same class, provided each image in the class contains set patterns that resemble each other. Image classification then reduces to determining if a set pattern in a test image is near a set pattern in a query image. Show more

Keywords: Descriptive set pattern, EF-proximity, Grenander pattern generator, near sets, proximally continuous function, proximity space, uniform topology

DOI: 10.3233/FI-2013-914

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 357-367, 2013

Authors: Nguyen, Sinh Hoa | Nguyen, Tuan Trung | Szczuka, Marcin | Nguyen, Hung Son

Article Type: Research Article

Abstract: This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last …10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification. Show more

Keywords: Concept approximation, granular computing, layered learning, hand-written digits, object identification, sunspot recognition, pattern recognition, classification

DOI: 10.3233/FI-2013-915

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 369-384, 2013

Authors: Liu, Yuchao | Li, Deyi | He, Wen | Wang, Guoyin

Article Type: Research Article

Abstract: Granular computing is one of the important methods for extracting knowledge from data and has got great achievements. However, it is still a puzzle for granular computing researchers to imitate the human cognition process of choosing reasonable granularities automatically for dealing with difficult problems. In this paper, a Gaussian cloud transformation method is proposed to solve this problem, which is based on Gaussian Mixture Model and Gaussian Cloud Model. Gaussian Mixture Model (GMM) is used to transfer an original data set to a sum of Gaussian distributions, and Gaussian Cloud Model (GCM) is used to represent the extension of a …concept and measure its confusion degree. Extensive experiments on data clustering and image segmentation have been done to evaluate this method and the results show its performance and validity. Show more

Keywords: Granular computing, Gaussian Mixture Model, Gaussian Cloud Model, Data clustering, Image segmentation

DOI: 10.3233/FI-2013-916

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 385-398, 2013

Authors: Pedrycz, Witold

Article Type: Research Article

Abstract: Fuzzy sets (membership functions) are numeric constructs. In spite of the underlying semantics of fuzzy sets (which is inherently linked with the higher level of abstraction), the membership grades and processing of fuzzy sets themselves emphasize the numeric facets of all pursuits stressing the numeric nature of membership grades and in this way reducing the interpretability and transparency of results. In this study, we advocate an idea of a granular description of membership functions where instead of numeric membership grades, introduced are more interpretable granular descriptors (say, low, high membership, etc.). Granular descriptors are formalized with the aid of various …formal schemes available in Granular Computing, especially sets (intervals), fuzzy sets, and shadowed sets. We formulate a problem of a design of granular descriptors as a certain optimization task, elaborate on the solutions and highlight some areas of applications. Show more

Keywords: Granular Computing, granular description of membership, rough sets, shadowed sets, optimization, granular fuzzy modeling

DOI: 10.3233/FI-2013-917

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 399-412, 2013

Authors: Lin, Tsau Young | Liu, Yong | Huang, Wenliang

Article Type: Research Article

Abstract: This paper explains the mathematics of large scaled granular computing (GrC), augmented with a new Knowledge theory, by unifying rough set theories (RS) into one single concept, namely, neighborhood systems (NS). NS was first introduced in 1989 by T. Y. Lin to capture the concepts of “near” (topology) and “conflict” (security). Since 1996 when the term Granular Computing (GrC) was coined by T. Y. Lin to label Zadeh's vision, NS has been pushed into the “heart” of GrC. In 2011, LNS, the largest NS, was axiomatized; it implied that this set of axioms defines a new mathematics that realizes Zadeh's …vision. The main messages are: this new mathematics is powerful and practical. Show more

Keywords: granular computing, neighborhood system, central knowledge, rough set, topological space, variable precision rough set

DOI: 10.3233/FI-2013-918

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 413-428, 2013

Authors: Stepaniuk, Jaroslaw | Kopczynski, Maciej | Grzes, Tomasz

Article Type: Research Article

Abstract: In this paper we propose a combination of capabilities of the FPGA based device and PC computer for data processing using rough set methods. Presented architecture has been tested on the exemplary data sets. Obtained results confirm the significant acceleration of the computation time using hardware supporting rough set operations in comparison to software implementation.

DOI: 10.3233/FI-2013-919

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 429-443, 2013

Authors: Ślęzak, Dominik | Synak, Piotr | Wojna, Arkadiusz | Wróblewski, Jakub

Article Type: Research Article

Abstract: We present analytic data processing technology derived from the principles of rough sets and granular computing. We show how the idea of approximate computations on granulated data has evolved toward complete product supporting standard analytic database operations and their extensions. We refer to our previous works where our query execution algorithms were described in terms of iteratively computed rough approximations. We explain how to interpret our data organization methods in terms of classical rough set notions such as reducts and generalized decisions.

Keywords: Analytic Data Processing Systems, Rough-Granular Computational Models

DOI: 10.3233/FI-2013-920

Citation: Fundamenta Informaticae, vol. 127, no. 1-4, pp. 445-459, 2013