Rough set approach for categorical data clustering

A few techniques of rough categorical data clustering exist to group objects having similar characteristics. However, the performance of the techniques is an issue due to low accuracy, high computational complexity and clusters purity. This work proposes a new technique called Maximum Dependency Attributes (MDA) to improve the previous techniques due to these issues. The proposed technique is based on rough set theory by taking into account the dependency of attributes of an information system. The main contribution of this technique is to introduce a new technique to classify objects from categorical datasets which has better performance as compared to the baseline techniques. The algorithm of the proposed technique is implemented in MATLAB® version 7.6.0.324 (R2008a). They are executed sequentially on a processor Intel Core 2 Duo CPUs. The total main memory is 1 Gigabyte and the operating system is Windows XP Professional SP3. Results collected during the experiments on four small datasets and thirteen UCI benchmark datasets for selecting a clustering attribute show that the proposed MDA technique is an efficient approach in terms of accuracy and computational complexity as compared to BC, TR and MMR techniques. For the clusters purity, the results on Soybean and Zoo datasets show that MDA technique provided better purity up to 17% and 9%, respectively. The experimental result on supplier chain management clustering also demonstrates how MDA technique can contribute to practical system and establish the better performance for computation complexity and clusters purity up to 90% and 23%, respectively

An Integrated Information System to Support Supply Chain Management & Performance in SMEs

Purpose: This study aims to examine the relation between the level of supply chain management (SCM) adoption and small & medium enterprises (SMEs) performance. SCM adoption level it is expected to facilitate SMEs in improving their efficiency, thus they can obtain their competitive advantage. Design/methodology/approach: This study uses primary data in the form of questionnaires. This study only takes the SMEs engaged in commerce (retail) business in order to avoid bias in IT usage. The questionnaires are given to 88 SMEs owners whom responsible for the IT development in their companies. Findings: The result proves that SCM adoption significantly affects SMEs performance. The hypotheses testing is performed using one-way ANOVA, the result shows that there are significant differences between level initiation, diffusion, and integration with SMEs performance. Originality/value: This study explains the relation between supply chain management (SCM) adoption level and SMEs performance that has never been performed before.Peer Reviewe

A Design of Educational Multimedia Software for Disability: A Case Study for Deaf People

This paper focuses on develop a new multimedia courseware for disabilities students mainly for students who have hearing problem. This application can be used by deaf pupils to learn sign language by watching video and animation pictures to communicate with other deaf people. This system will be providing the student to select the alphabetical and see the picture and the sign language explanation. The student also allows selecting the number and picture to view and with the sign language explanation. With the navigation provide to e-MSL allow the student to access with easily

Privacy preserving social network data publication

The introduction of online social networks (OSN) has transformed the way people connect and interact with each other as well as share information. OSN have led to a tremendous explosion of network-centric data that could be harvested for better understanding of interesting phenomena such as sociological and behavioural aspects of individuals or groups. As a result, online social network service operators are compelled to publish the social network data for use by third party consumers such as researchers and advertisers. As social network data publication is vulnerable to a wide variety of reidentification and disclosure attacks, developing privacy preserving mechanisms are an active research area. This paper presents a comprehensive survey of the recent developments in social networks data publishing privacy risks, attacks, and privacy-preserving techniques. We survey and present various types of privacy attacks and information exploited by adversaries to perpetrate privacy attacks on anonymized social network data. We present an in-depth survey of the state-of-the-art privacy preserving techniques for social network data publishing, metrics for quantifying the anonymity level provided, and information loss as well as challenges and new research directions. The survey helps readers understand the threats, various privacy preserving mechanisms, and their vulnerabilities to privacy breach attacks in social network data publishing as well as observe common themes and future directions

Investigating rendering speed and download rate of three-dimension (3D) mobile map intended for navigation aid using genetic algorithm

Prior studies have shown that rendering 3D map dataset in mobile device in a wireless network depends on the download speed. Crucial to that is the mobile device computing resource capabilities. Now it has become possible with a wireless network to render large and detailed 3D map of cities in mobile devices at interactive rates of over 30 frame rate per second (fps). The information in 3D map is generally limited and lack interaction when it’s not rendered at interactive rate; on the other hand, with high download rate 3D map is able to produce a realistic scene for navigation aid. Unfortunately, in most mobile navigation aid that uses a 3D map over a wireless network could not serve the needs of interaction, because it suffers from low rendering speed. This paper investigates the trade-off between rendering speed and download rate of the 3D mobile map using genetic algorithm (GA). The reason of using GA is because it takes larger problem space than other algorithms for optimization, which is well suited for establishing fast 3D map rendering speed on-the-fly to the mobile device that requires useful solutions for optimization. Regardless of mobile device’s computing resources, our finding from GA suggest that download rate and rendering speed are mutually exclusive. Thus, manipulated static aerial photo-realistic images instead of 3D map are well-suited for navigation aid

A Comprehensive Survey on Comparisons across Contextual Pre-filtering, Contextual Post-filtering and Contextual Modelling Approaches

Recently, there has been growing interest in recommender systems (RS) and particularly in context-aware RS. Methods for generating context-aware recommendations are classified into pre-filtering, post-filtering and contextual modelling approaches. In this paper, we present the several novel approaches of the different variant of each of these three contextualization paradigms and present a complete survey on the state-of-the-art comparisons across them. We then identify the significant challenges that require being addressed by the current RS researchers, which will help academicians and practitioners in comparing these three approaches to select the best alternative according to their strategies

A Framework of Clustering Based on Chicken Swarm Optimization

Chicken Swarm Optimization (CSO) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behaviour of chicken swarm. Data clustering is used in many disciplines and applications. It is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, CSO is used for data clustering. The performance of the proposed CSO was assessed on several data sets and compared with well known and recent metaheuristic algorithm for clustering: Particle Swarm Optimization (PSO) algorithm , Cuckoo Search (CS) and Bee Colony Algorithm (BC). The simulation results indicate that CSO algorithm have much potential and can efficiently be used for data clustering

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 computa-tional time and low clustering accuracy. Moreover, the parameter maximize of the classification like-lihood 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 indiscern-ibility 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 multi-variate multinomial distributions, the proposed approach is based on indescernibility relation. We per-formed 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 bench-mark 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

A review on soft set-based parameter reduction and decision making

Many real world decision making problems often involve uncertainty data, which mainly originating from incomplete data and imprecise decision. The soft set theory as a mathematical tool that deals with uncertainty, imprecise, and vagueness is often employed in solving decision making problem. It has been widely used to identify irrelevant parameters and make reduction set of parameters for decision making in order to bring out the optimal choices. In this paper, we present a review on different parameter reduction and decision making techniques for soft set and hybrid soft sets under unpleasant set of hypothesis environment as well as performance analysis of the their derived algorithms. The review has summarized this paper in those areas of research, pointed out the limitations of previous works and areas that require further research works. Researchers can use our review to quickly identify areas that received diminutive or no attention from researchers so as to propose novel methods and applications

A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets