Rabiei Mamat

MAR: Maximum Attribute Relative of soft set for clustering attribute selection

Clustering, which is a set of categorical data into a homogenous class, is a fundamental operation in data mining. One of the techniques of data clustering was performed by introducing a clustering attribute. A number of algorithms have been proposed to address the problem of clustering attribute selection. However, the performance of these algorithms is still an issue due to high computational complexity. This paper proposes a new algorithm called Maximum Attribute Relative (MAR) for clustering attribute selection. It is based on a soft set theory by introducing the concept of the attribute relative in information systems. Based on the experiment on fourteen UCI datasets and a supplier dataset, the proposed algorithm achieved a lower computational time than the three rough set-based algorithms, i.e. TR, MMR, and MDA up to 62%, 64%, and 40% respectively and compared to a soft set-based algorithm, i.e. NSS up to 33%. Furthermore, MAR has a good scalability, i.e. the executing time of the algorithm tends to increase linearly as the number of instances and attributes are increased respectively.

A new limited tolerance relation for attribute selection in incomplete information systems

Classical rough set theory has been used in analyzing complete information systems, where all attribute values are available to all objects. However, it cannot cope with the incomplete information systems where some attribute values are not available or missing. Subsequently, the attribute selection is one of the main problems in incomplete information systems. Only few studies were proposed for the attribute selection problem in incomplete information systems due to its complexities, specifically on attribute selection. The most popular approaches are based on the extensions of classical rough set theory where it is relaxed by non-symmetric similarity relation and limited tolerance relation. From these two approaches, limited tolerance relation is more favorable. However, the approach has its weaknesses from the issues of imprecise and accuracy to evaluate data classification in incomplete information systems. To overcome these issues, we propose a new limited tolerance relation in rough set using conditional entropy to handle flexibility and precisely data classification. The novelty of the approach is that, unlike previous approach that use limited tolerance relation, it takes into consideration the similarity precision between objects in incomplete information systems and therefore this is the first work that used similarity precision. We also compared the proposed approach with limited tolerance relation approach, and the results show that the proposed approach achieves higher accuracy in the process of attribute selection in incomplete information systems.

Super Attribute Representative for Decision Attribute Selection

Soft set theory proposed by Molodstov is a general mathematic tool for dealing with uncertainties. Recently, several algorithms had been proposed for decision making using soft set theory. However, these algorithms still concern on a Boolean-valued information system. In this paper, Support Attribute Representative (SAR), a soft set based technique for decision making in categorical-valued information system is proposed. The proposed technique has been tested on two datasets. The results of this research will provide useful information for decision makers to handle categorical datasets.

Cluster Validation Analysis on Attribute Relative of Soft-Set Theory

Data clustering on categorical data pose a difficult challenge since there are no-inherent distance measures between data values. One of the approaches that can be used is by introducing a series of clustering attributes in the categorical data. By this approach, Maximum Total Attribute Relative (MTAR) technique that is based on the attribute relative of soft-set theory has been proposed and proved has better execution time as compared to other equivalent techniques that used the same approach. In this paper, the cluster validity analysis on the technique is explained and discussed. In this analysis, the validity of the clusters produced by MTAR technique is evaluated by the entropy measure using two standards dataset: Soybean (Small) and Zoo from University California at Irvine (UCI) repository. Results show that the clusters produce by MTAR technique have better entropy and improved the clusters validity up to 33%.

On soft partition attribute selection

Rough set theory provides a methodology for data analysis based on the approximation of information systems. It is revolves around the notion of discernibility i.e. the ability to distinguish between objects based on their attributes value. It allows inferring data dependencies that are useful in the fields of feature selection and decision model construction. Since it is proven that every rough set is a soft set, therefore, within the context of soft sets theory, we present a soft set-based framework for partition attribute selection. The paper unifies existing work in this direction, and introduces the concepts of maximum attribute relative to determine and rank the attribute in the multi-valued information system. Experimental results demonstrate the potentiality of the proposed technique to discover the attribute subsets, leading to partition selection models which better coverage and achieve lower computational time than that the baseline techniques.

SAR: An Algorithm for Selecting a Partition Attribute in Categorical-Valued Information System Using Soft Set Theory

Soft-set theory proposed by Molodstov is a general mathematic tool for dealing with uncertainty. Recently, several algorithms have been proposed for decision making using soft-set theory. However, these algorithms still concern on Boolean-valued information system. In this paper, Support Attribute Representative SAR, a soft-set based technique for decision making in categorical-valued information system is proposed. The proposed technique has been tested on three datasets to select the best partitioning attribute. Furthermore, two UCI benchmark datasets are used to elaborate the performance of the proposed technique in term of executing time. On these two datasets, it is shown that SAR outperforms three rough set-based techniques TR, MMR, and MDA up to 95% and 50%, respectively. The results of this research will provide useful information for decision makers to handle categorical datasets.

High reliability replication technique for web-server cluster systems

Providing reliable and efficient services are primary goals in designing a web server system. Data replication can be used to improve the reliability of the system. However, mapping mechanism is one of the primary concerns to data replication. In this paper, we propose a mapping mechanism model called enhanced domain name server (E-DNS) that dispatches the user requests through the URL-name to IP-address under Neighbor Replica Distribution Technique (NRDT) to improve the reliability of the system.