Xiuqin Ma

A new efficient normal parameter reduction algorithm of soft sets

Kong et al. [Kong, Z., Gao, L., Wang, L., and Li, S., The normal parameter reduction of soft sets and its algorithm, Computers and Mathematics with Applications 56 (12) (2008) 3029-3037] introduced the definition of normal parameter reduction in soft sets and presented a heuristic algorithm of normal parameter reduction. However, the algorithm is hard to understand and involves a great amount of computation. In this paper, firstly, we give some new related definitions and proved theorems of normal parameter reduction. Then we propose a new efficient normal parameter reduction algorithm of soft sets based on the oriented-parameter sum, which can be carried out without parameter important degree and decision partition. The comparison result on a dataset shows that the proposed algorithm involves relatively less computation and is easier to implement and understand as compared with the algorithm of normal parameter reduction proposed by Kong et al.

A novel soft set approach in selecting clustering attribute

Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data. One of the techniques of data clustering was performed by introducing a clustering attribute. Soft set theory, initiated by Molodtsov in 1999, is a new general mathematical tool for dealing with uncertainties. In this paper, we define a soft set model on the equivalence classes of an information system, which can be easily applied in obtaining approximate sets of rough sets. Furthermore, we use it to select a clustering attribute for categorical datasets and a heuristic algorithm is presented. Experiment results on fifteen UCI benchmark datasets showed that the proposed approach provides a faster decision in selecting a clustering attribute as compared with maximum dependency attributes (MDAs) approach up to 14.84%. Furthermore, MDA and NSS have a good scalability i.e. the executing time of both algorithms tends to increase linearly as the number of instances and attributes are increased, respectively.

Data filling approach of soft sets under incomplete information

Incomplete information in a soft set restricts the usage of the soft set. To make the incomplete soft set more useful, in this paper, we propose a data filling approach for incomplete soft set in which missing data is filled in terms of the association degree between the parameters when stronger association exists between the parameters or in terms of the probability of objects appearing in the mapping sets of parameters when no stronger association exists between the parameters. An illustrative example is employed to show the feasibility and validity of our approach in practical applications.

An adjustable approach to interval-valued intuitionistic fuzzy soft sets based decision making

Research on soft set based decision making has received much attention in recent years. Feng et al. presented an adjustable approach to fuzzy soft sets based decision making by using level soft set, and subsequently extended the approach to interval-valued fuzzy soft set based decision making. Jiang et al. generalize the approach to solve intuitionistic fuzzy soft sets based decision making. In this paper, we further generalize the approaches introduced by Feng et al. and Jiang et al. Using reduct intuitionistic fuzzy soft sets and level soft sets of intuitionistic fuzzy soft sets, an adjustable approach to intervalvalued intuitionistic fuzzy soft set based decision making is presented. Some illustrative example is employed to show the feasibility of our approach in practical applications.

MGR: An information theory based hierarchical divisive clustering algorithm for categorical data

Categorical data clustering has attracted much attention recently due to the fact that much of the data contained in todays databases is categorical in nature. While many algorithms for clustering categorical data have been proposed, some have low clustering accuracy while others have high computational complexity. This research proposes mean gain ratio (MGR), a new information theory based hierarchical divisive clustering algorithm for categorical data. MGR implements clustering from the attributes viewpoint which includes selecting a clustering attribute using mean gain ratio and selecting an equivalence class on the clustering attribute using entropy of clusters. It can be run with or without specifying the number of clusters while few existing clustering algorithms for categorical data can be run without specifying the number of clusters. Experimental results on nine University of California at Irvine (UCI) benchmark and ten synthetic data sets show that MGR performs better as compared to baseline algorithms in terms of its performance and efficiency of clustering.

The Parameter Reduction of the Interval-Valued Fuzzy Soft Sets and Its Related Algorithms

There has been a rapid growth of interest in developing approaches that are capable of dealing with imprecision and uncertainty. To this end, an interval-valued fuzzy soft set (IVFSS) that combines soft set theory with interval-valued fuzzy set theory has been proposed to handle imprecision and uncertainty in applications such as decision-making problems. However, there has been little focus on parameter reduction of the interval-valued fuzzy soft sets, which is significant in decision-making problems. In this paper, we introduce four different definitions of parameter reduction in interval-valued fuzzy soft sets to satisfy different the needs of decision makers. We propose four heuristic algorithms of parameter reduction. Finally, the algorithms are compared and summarized from the aspects of easy degree of finding reduction, applicability, reduction result, exact level for reduction, multiusability, applied situation, and computational complexity. The results of the experiment show that the methods reduce the redundant parameters while preserving certain decision abilities.

DFIS: A novel data filling approach for an incomplete soft set

The research on incomplete soft sets is an integral part of the research on soft sets and has been initiated recently. However, the existing approach for dealing with incomplete soft sets is only applicable to decision making and has low forecasting accuracy. In order to solve these problems, in this paper we propose a novel data filling approach for incomplete soft sets. The missing data are filled in terms of the association degree between the parameters when a stronger association exists between the parameters or in terms of the distribution of other available objects when no stronger association exists between the parameters. Data filling converts an incomplete soft set into a complete soft set, which makes the soft set applicable not only to decision making but also to other areas. The comparison results elaborated between the two approaches through UCI benchmark datasets illustrate that our approach outperforms the existing one with respect to the forecasting accuracy.

QoS-Aware Web Services Selection with Interval-Valued Intuitionistic Fuzzy Soft Sets

With the increasing popularity of the development of service-oriented applications, it is imperative to measure the quality of services (QoS) for service consumers and providers. To find the most suitable service for different consumers, the QoS nonfunctional attribute will become an important factor in web service selection. However, non-functional QoS properties rely heavily on the subjective perceptions of service consumers that are not easy to assess due to their complexity and involvement of ill-structured information. The purpose of this paper is to introduce interval-valued intuitionistic fuzzy soft set theory for solving web service selection problems that take into account QoS requirement of consumers. Interval-valued intuitionistic fuzzy soft set theory, which is a new useful mathematical tool for dealing with uncertainties, is more effective to deal with uncertainties on non-functional QoS properties in web service selection. We present basic system architecture and the algorithm to solve fuzzy decision making problems for selecting web service based on interval-valued intuitionistic fuzzy soft sets. Finally, an illustrative example is employed to show our contribution.

A survey of query expansion, query suggestion and query refinement techniques

The ineffectiveness of information retrieval systems often caused by the inaccurate use of keywords in a query. In order to solve the ineffectiveness problem in information retrieval systems, many solutions have been proposed over the years. The most common techniques are revolving around query modification techniques such as query expansion, query refinement, etc. Due to the high similarity in these query modification techniques, people are often confused about their differences. However, few existing survey papers compare their differences. Hence, in this paper, we first briefly discuss the basic technique of query expansion, query suggestion and query refinement, and then make a detailed comparison between these three techniques. We finally show the promising future research trend in the field of query modification.

A Novel Normal Parameter Reduction Algorithm of Soft Sets

In this paper, we propose a novel normal parameter reduction algorithm of soft sets based on the oriented-parameter sum, which can be carried out without parameter important degree and decision partition. We present some new related definitions and proved theorems of normal parameter reduction. The comparison result on a Boolean-valued dataset shows that, the proposed algorithm involves relatively less computation and is easier to implement and understand as compared with the soft set-based algorithm of normal parameter reduction.