Mohd Isa Awang

Solving Incomplete Datasets in Soft Set Using Parity Bits of Supported Sets

The theory of soft set proposed by Molodtsov [2]in 1999 is a new method for handling uncertain data and can be redefined as a Boolean-valued information system. The soft set theory has been applied to data analysis and decision support systems based on large datasets. Using retrieved datasets, we have calculated the supported values and then determine the even parity bits. Using the parity bit, the problem of missing values from the retrieved datasets can be solved.

Hybrid reduction in soft set decision making

In this paper, we present an extended technique of decision making by implementing column reduction with reduction based on calculated maximal support objects. Using a Boolean valued information system, certain rows or objects can be defined as ultimate maximum support object, ultimate minimum support object and zero significance parameter. One can then reduce a table by eliminating the defined row or objects in what has been defined as hybrid reduction. As part of our paper, we have managed to show that our proposed model of hybrid reduction yielded a better data size reduction whilst still maintaining consistent results.

Solving Incomplete Datasets in Soft Set Using Supported Sets and Aggregate Values

Abstract The theory of soft set proposed by Molodtsovin 1999[1]is a new method for handling uncertain data and can be defined as a Boolean-valued information system. Ithas been applied to data analysis and decision support systems based on large datasets. In this paper, it is shown that calculated support value can be used to determine missing attribute value of an object. However, in cases when more than one value is missing, the aggregate values and calculated support values will be used in determining the missing values. By successfully recovering missing attribute values, the integrity of a dataset can still been maintained.

Soft set approach for selecting decision attribute in data clustering

This paper presents the applicability of soft set theory for discovering a decision attribute in information systems. It is based on the notion of a mapping inclusion in soft set theory. The proposed technique is implemented with example test case and one UCI benchmark data; US Census 1990 dataset. The results from test case show that the selected decision attribute is equivalent to that under rough set theory.

Silhouette index for determining optimal k-means clustering on images in different color models

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.

Algorithms for extracting adjacency matrix based on formal concept analysis (FCA)

The desire to achieve a holistic representation of Information Retrieval (IR) with the aim for a human-oriented form of representation has spurred the growth of concept-based IR search techniques such as the Semantic Web technology. However, Semantic Web calls for the use of ontologies for many domains. Although meaningful and important, ontology development presents great challenges to the developers especially in terms of conceptual dynamics.. This paper is based on a study that attempts to provide an alternative to ontology lookup for Semantic information retrieval. However, the focus of the paper is on a method proposed to extract adjacency matrix from concepts obtained from the theory of Formal Concept Analysis (FCA) using two consecutive algorithms called the Relatedness Algorithm and Adjacency Matrix Algorithm. Consequently, the adjacency matrices obtained could be used in a similarity measure process based on graph theory. The proposed method offers an alternative to specific domain ontology look-up where results from the measure can further be used in concept-based IR process.

Multiple Criteria Preference Relation by Dominance Relations in Soft Set Theory

This paper presents the applicability of soft set theory for discovering the preference relation in multi-valued information systems. The proposed approach is based on the notion of multi-soft sets. An inclusion of objects into value set of decision class in soft set theory is used to discover the relation between objects based on preference relation. Results from the experiment shows that dominance relation based on soft theory for preference relation is able to produce a finer object classification by eliminating inconsistencies during classification process as opposed to the expert judgement classification.