Fundamenta Informaticae - Volume 115, issue 2-3

Authors: Wang, Guoyin | Skowron, Andrzej | Yao, Yiyu | Yu, Hong

Article Type: Other

DOI: 10.3233/FI-2012-645

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. i-ii, 2012

Authors: Yu, Hong | Chu, Shuangshuang | Yang, Dachun

Article Type: Research Article

Abstract: In many applications, clusters tend to have vague or imprecise boundaries. It is desirable that clustering techniques should consider such an issue. The decision-theoretic rough set (DTRS) model is a typical probabilistic rough set model, which has the ability to deal with imprecise, uncertain, and vague information. This paper proposes an autonomous clustering method using the decision-theoretic rough set model based on a knowledge-oriented clustering framework. In order to get the initial knowledge-oriented clustering, the threshold values are produced autonomously based on semantics of clustering without human intervention. Furthermore, this paper estimates the risk of a clustering scheme based on …the decision-theoretic rough set by considering various loss functions, which can process the different granular overlapping boundary. An autonomous clustering algorithm is proposed, which is not only experimented with the synthetic data and the standard data but also applied in the web search results clustering. The results of experiments show that the proposed method is effective and efficient. Show more

Keywords: clustering, knowledge-oriented clustering, decision-theoretic rough set theory, autonomous, data mining

DOI: 10.3233/FI-2012-646

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 141-156, 2012

Authors: Yang, Xiaoping | Yao, JingTao

Article Type: Research Article

Abstract: The decision-theoretic rough set (DTRS) model considers costs associated with actions of classifying an equivalence class into a particular region. With DTRS, one may make informative decisions in the form of three-way decisions. Current research mainly focuses on single agent DTRS which is too complex for making a decision when multiple agents are involved. We propose a multi-agent DTRS model and express it in the form of three-way decisions. The new model seeks for synthesized or consensus decisions when there are multiple decision preferences and criteria adopted by different agents. Various multi-agent DTRS models can be derived according to the …conservative, aggressive and majority viewpoints based on the positive, negative and boundary regions made by each agent. These multi-agent decision regions are expressed by figures in the form of three-way decisions. Show more

Keywords: Decision-theoretic rough sets, three-way decisions, probabilistic rough sets, multi-agent decision makings

DOI: 10.3233/FI-2012-647

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 157-171, 2012

Authors: Liu, Dun | Li, Tianrui | Li, Huaxiong

Article Type: Research Article

Abstract: By considering the levels of tolerance for errors and the cost of actions in real decision procedure, a new two-stage approach is proposed to solve the multiple-category classification problems with Decision-Theoretic Rough Sets (DTRS). The first stage is to change an m-category classification problem (m > 2) into an m two-category classification problem, and form three types of decision regions: positive region, boundary region and negative region with different states and actions by using DTRS. The positive region makes a decision of acceptance, the negative region makes a decision of rejection, and the boundary region makes a decision of abstaining. …The second stage is to choose the best candidate classification in the positive region by using the minimum probability error criterion with Bayesian discriminant analysis approach. A case study of medical diagnosis demonstrates the proposed method. Show more

Keywords: Decision-theoretic rough sets, probabilistic rough sets, bayesian decision procedure, three-way decisions, multiple-category

DOI: 10.3233/FI-2012-648

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 173-188, 2012

Authors: An, Shuang | Hu, Qinghua | Yu, Daren | Liu, Jinfu

Article Type: Research Article

Abstract: The theory of fuzzy rough sets is claimed to be a powerful mathematical tool for dealing with uncertainty in data analysis. Unluckily, the classical model of fuzzy rough sets is sensitive to noisy information. This disadvantage limits the applicability of the model in practice. In this work, we present a robust fuzzy rough set model based on soft minimum enclosing ball, and introduce a new fuzzy dependency function with this model. Some properties of the new model are discussed. Finally, we conduct some experiments to test the effectiveness of the proposed model, and experimental results show that the soft minimum …enclosing ball-based fuzzy rough set model is robust to noise. Show more

Keywords: Fuzzy rough sets, soft minimum enclosing ball, robustness, fuzzy dependency

DOI: 10.3233/FI-2012-649

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 189-202, 2012

Authors: Wu, Wei-Zhi

Article Type: Research Article

Abstract: Statisticians and database users often encounter the problem of missing or imprecise data obtained by a random experiment. Such a data set is called a random incomplete information table. In this paper, we study knowledge reduction in random incomplete information tables and random incomplete decision tables by using a hybrid model based on the rough set theory and the Dempster-Shafer theory of evidence. The concepts of random belief reducts and random plausibility reducts in random incomplete information tables and random incomplete decision tables are introduced. The relationships among the lower approximation reduct, the upper approximation reduct, the random belief reduct, …the random plausibility reduct, and the classical reduct in random incomplete decision tables are examined. Show more

Keywords: Belief functions, knowledge reduction, random incomplete decision tables, random incomplete information tables, random sets, rough sets

DOI: 10.3233/FI-2012-650

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 203-218, 2012

Authors: Wang, GuoYin | Hu, Jun

Article Type: Research Article

Abstract: The concept of the complement of a covering is introduced, and then the extended space of a covering approximation space is induced based on it. Generally, the extended space of a covering approximation space generates a bigger covering lower approximation or smaller covering upper approximation than itself. Through extending each covering of a covering decision system, the classification ability of each covering may be improved. Thus, a heuristic reduction algorithm is developed to eliminate some coverings in a covering decision system without decreasing the classification ability of the system for decision. Theoretical analysis and experimental results indicate that this algorithm …can often get smaller reduction than other algorithms. Show more

Keywords: covering, rough set, covering decision system, attribute reduction

DOI: 10.3233/FI-2012-651

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 219-232, 2012

Authors: Joshi, Manish | Lingras, Pawan | Raghavendra Rao, C.

Article Type: Research Article

Abstract: With the gaining popularity of rough clustering, soft computing research community is studying relationships between rough and fuzzy clustering as well as their relative advantages. Both rough and fuzzy clustering are less restrictive than conventional clustering. Fuzzy clustering memberships are more descriptive than rough clustering. In some cases, descriptive fuzzy clustering may be advantageous, while in other cases it may lead to information overload. Many applications demand use of combined approach to exploit inherent strengths of each technique. Our objective is to examine correlation between these two techniques. This paper provides an experimental description of how rough clustering results can …be correlated with fuzzy clustering results. We illustrate procedural steps to map fuzzy membership clustering to rough clustering. However, such a conversion is not always necessary, especially if one only needs lower and upper approximations. Experiments also show that descriptive fuzzy clustering may not always (particularly for high dimensional objects) produce results that are as accurate as direct application of rough clustering. We present analysis of the results from both the techniques. Show more

Keywords: Rough Clustering, Fuzzy Clustering, Rough K-means, Fuzzy C-means, FuzzyRough Correlation Factors, Cluster Quality

DOI: 10.3233/FI-2012-652

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 233-246, 2012

Authors: Yao, Yiyu | Zhang, Nan | Miao, Duoqian | Xu, Feifei

Article Type: Research Article

Abstract: A framework is proposed for studying a particular class of set-theoretic approaches to granular computing. A granule is a subset of a universal set, a granular structure is a family of subsets of the universal set, and relationship between granules is given by the standard set-inclusion relation. By imposing different conditions on the family of subsets, we can define several types of granular structures. A number of studies, including rough set analysis, formal concept analysis and knowledge spaces, adopt specific models of granular structures. The proposed framework therefore provides a common ground for unifying these studies. The notion of approximations …is examined based on granular structures. Show more

Keywords: Granular computing, Granular structures, Rough set analysis, Formal concept analysis, Knowledge spaces

DOI: 10.3233/FI-2012-653

Citation: Fundamenta Informaticae, vol. 115, no. 2-3, pp. 247-264, 2012