Iwan Tri Riyadi Yanto

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.

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.

SMARViz: Soft Maximal Association Rules Visualization

Maximal association rule is one of the popular data mining techniques. However, no current research has found that allow for the visualization of the captured maximal rules. In this paper, SMARViz (Soft Maximal Association Rules Visualization ), an approach for visualizing soft maximal association rules is proposed. The proposed approach contains four main steps, including discovering, visualizing maximal supported sets, capturing and finally visualizing the maximal rules under soft set theory.

Soft Set Approach for Maximal Association Rules Mining

In this paper, an alternative approach for maximal association rules mining from a transactional database using soft set theory is proposed. The first step of the proposed approach is based on representing a transactional database as a soft set. Based on the soft set, the notion of items co-occurrence in a transaction can be defined. The definitions of soft maximal association rules, maximal support and maximal confidence are presented using the concept of items co-occurrence. It is shown that by using soft set theory, maximal rules discovered are identical and faster as compared to traditional maximal and rough maximal association rules approaches.

Data clustering using variable precision rough set

Clustering a set of objects into homogeneous classes is a fundamental operation in data mining. Several cluster analysis techniques have been developed to group objects having similar characteristics. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. An algorithm termed MMR using classical rough set theory was proposed to deal with problems in clustering categorical data. However, the MMR algorithm fails to handle noisy data as an integral part of databases. In this paper, an alternative technique for clustering noisy categorical data using Variable Precision Rough Set model is proposed. The results show that the technique provides better performance in selecting the clustering attribute.

A Construction of Hierarchical Rough Set Approximations in Information Systems Using Dependency of Attributes

This paper presents an alternative approach for constructing a hierarchical rough set approximation in an information system. It is based on the notion of dependency of attributes. The proposed approach is started with the notion of a nested sequence of indiscernibility relations that can be defined from the dependency of attributes. With this notion, a nested rough set approximation can be easily constructed. Then, the notion of a nested rough set approximation is used for constructing a hierarchical rough set approximation. Lastly, applications of a hierarchical rough set approximation for data classification and capturing maximal association in document collection through information systems are presented.

Rough Set Approach for Attributes Selection of Traditional Malay Musical Instruments Sounds Classification

Feature selection has become very important research in musical instruments sounds for handling the problem of ‘curse of dimensionality’. In literature, various feature selection techniques have been applied in this domain focusing on Western musical instruments sounds. However, study on feature selection using rough sets of non-Western musical instruments sounds including Malay Traditional musical instruments is inadequate and still needs an intensive research. Thus, in this paper, an alternative feature selection technique using rough set theory based on Maximum Degree of dependency of Attributes (MDA) technique proposed by [8] for Traditional Malay musical instruments sounds is proposed. The modeling process comprises eight phases: data acquisition, sound editing, data representation, feature extraction, data discretization, data cleansing, feature selection using proposed technique and feature validation via classification. The results show that the highest classification accuracy of 99.82% was achieved from the best 17 features with 1-NN classifier.

A modified Fuzzy k-Partition based on indiscernibility relation for categorical data clustering

Categorical data clustering has been adopted by many scientific communities to classify objects from large databases. In order to classify the objects, Fuzzy k-Partition approach has been proposed for categorical data clustering. However, existing Fuzzy k-Partition approaches suffer from high computational time and low clustering accuracy. Moreover, the parameter maximize of the classification likelihood function in Fuzzy k-Partition approach will always have the same categories, hence producing the same results. To overcome these issues, we propose a modified Fuzzy k-Partition based on indiscernibility relation. The indiscernibility relation induces an approximation space which is constructed by equivalence classes of indiscernible objects, thus it can be applied to classify categorical data. The novelty of the proposed approach is that unlike previous approach that use the likelihood function of multivariate multinomial distributions, the proposed approach is based on indescernibility relation. We performed an extensive theoretical analysis of the proposed approach to show its effectiveness in achieving lower computational complexity. Further, we compared the proposed approach with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy on several UCI benchmark and real world datasets. The results show that the proposed approach achieves lower response time and higher clustering accuracy as compared to other Fuzzy k-based approaches.

Soft set theory for feature selection of traditional Malay musical instrument sounds

Computational models of the artificial intelligence such as soft set theory have several applications. Soft data reduction can be considered as a machine learning technique for features selection. In this paper, we present the applicability of soft set theory for feature selection of Traditional Malay musical instrument sounds. The modeling processes consist of three stages: feature extraction, data discretization and finally using the multi-soft sets approach for feature selection through dimensionality reduction in multivalued domain. The result shows that the obtained features of proposed model are 35 out of 37 attributes.

Application of Wavelet De-noising Filters in Mammogram Images Classification Using Fuzzy Soft Set

Recent advances in the field of image processing have revealed that the level of noise in mammogram images highly affect the images quality and classification performance of the classifiers. Whilst, numerous data mining techniques have been developed to achieve high efficiency and effectiveness for computer aided diagnosis systems. However, fuzzy soft set theory has been merely experimented for medical images. Thus, this study proposed a classifier based on fuzzy soft set with embedding wavelet de-noising filters. Therefore, the proposed methodology involved five steps namely: MIAS dataset, wavelet de-noising filters hard and soft threshold, region of interest identification, feature extraction and classification. Therefore, the feasibility of fuzzy soft set for classification of mammograms images has been scrutinized. Experimental results show that proposed classifier FussCyier provides the classification performance with Daub3 (Level 1) with accuracy 75.64% (hard threshold), precision 46.11%, recall 84.67%, F-Micro 60%. Thus, the results provide an alternative technique to categorize mammogram images.