Tutut Herawan

A rough set approach for selecting clustering attribute

A few of clustering techniques for categorical data exist to group objects having similar characteristics. Some are able to handle uncertainty in the clustering process while others have stability issues. However, the performance of these techniques is an issue due to low accuracy and high computational complexity. This paper proposes a new technique called maximum dependency attributes (MDA) for selecting clustering attribute. The proposed approach is based on rough set theory by taking into account the dependency of attributes of the database. We analyze and compare the performance of MDA technique with the bi-clustering, total roughness (TR) and min-min roughness (MMR) techniques based on four test cases. The results establish the better performance of the proposed approach.

A novel soft rough set

Graphical abstractDisplay Omitted In this paper, we investigate the relationships among rough sets, soft sets and hemirings. The concept of soft rough hemirings is introduced, which is an extended notion of a rough hemiring. It is pointed out that in this paper, we first apply soft rough sets to algebraic structure-hemirings. Further, we first put forward the concepts of C-soft sets and CC-soft sets, which provide a new research idea for soft rough algebraic research. Moreover, we study roughness in hemirings with respect to MSR-approximation spaces. Some new soft rough operations over hemirings are explored. In particular, lower and upper MSR-hemirings (k-ideal and h-ideal) are investigated. Finally, we put forth an approach for multicriteria group decision making problem based on modified soft rough sets and offer an actual example.

Big Data Clustering: A Review

Clustering is an essential data mining and tool for analyzing big data. There are difficulties for applying clustering techniques to big data duo to new challenges that are raised with big data. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This study is aimed to review the trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today’s novel solutions. The algorithms and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward the possible future path for more advanced algorithms is illuminated based on today’s available technologies and frameworks.

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.

Soft Set Theoretic Approach for Dimensionality Reduction

A reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of a given information system. The existing reduct approaches under soft set theory are still based on Boolean-valued information system. However, in the real applications, the data usually contain non-Boolean valued. In this paper, an alternative approach for attribute reduction in multi-valued information system under soft set theory is presented. Based on the notion of multi-soft sets and AND operation, attribute reduction can be defined. It is shown that the reducts obtained are equivalent with Pawlak’s rough reduction.

Applying variable precision rough set model for clustering student suffering study's anxiety

Computational models of the artificial intelligence such as rough set theory have several applications. Data clustering under rough set theory can be considered as a technique for medical decision making. One possible application is the clustering of student suffering studys anxiety. In this paper, we present the applicability of variable precision rough set model for clustering student suffering studies anxiety. The proposed technique is based on the mean of accuracy of approximation using variable precision of attributes. The datasets are taken from a survey aimed to identify of studies anxiety sources among students at Universiti Malaysia Pahang (UMP). At this stage of the research, we show how variable precision rough set model can be used to groups student in each studys anxiety. The results may potentially contribute to give a recommendation how to design intervention, to conduct a treatment in order to reduce anxiety and further to improve students academic performance.

A Direct Proof of Every Rough Set is a Soft Set

The purpose of this paper is devoted to revealing interconnection between rough sets and soft sets. We use the constructive and descriptive approaches of rough set theory and present a direct proof that Pawlak’s and Iwinski’s rough sets can be considered as soft sets.

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.

Mining significant least association rules using fast SLP-growth algorithm

Development of least association rules mining algorithms are very challenging in data mining. The complexity and excessive in computational cost are always become the main obstacles as compared to mining the frequent rules. Indeed, most of the previous studies still adopting the Apriori-like algorithms which are very time consuming. To address this issue, this paper proposes a scalable trie-based algorithm named SLP-Growth. This algorithm generates the significant patterns using interval support and determines its correlation. Experiments with the real datasets show that the SLP-algorithm can discover highly positive correlated and significant of least association. Indeed, it also outperforms the fast FP-Growth algorithm up to two times, thus verifying its efficiency.

Rough Set Approach for Categorical Data Clustering

In this paper, we focus our discussion on the rough set approach for categorical data clustering. We propose MADE (Maximal Attributes Dependency), an alternative technique for categorical data clustering using rough set theory taking into account maximal attributes dependencies. Experimental results on two benchmark UCI datasets show that MADE technique is better with the baseline categorical data clustering techniques with respect to computational complexity and clusters purity.