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Forthcoming Articles

These articles have been peer-reviewed and accepted for publication in JICT, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the JICT standard. Additionally, titles, authors, abstracts and keywords may change before publication.


EXTENSIONS TO THE K-AMH ALGORITHM FOR NUMERICAL CLUSTERING
Ali Seman and Azizian Mohd Sapawi
Center for Computer Science Studies, Faculty of Computer and Mathematical Sciences,
Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
aliseman@tmsk.uitm.edu.my; azizian@tmsk.uitm.edu.my

Abstract
The k-AMH algorithm has been proven efficient in clustering categorical datasets. It can also be used for numerical clustering with minimum modification to its original algorithm. In this paper, we present two algorithms that extend the k-AMH algorithm for numerical clustering. The original k-AMH algorithm for categorical clustering uses a simple matching dissimilarity measure but for numerical values it uses Euclidean distance. The first extension to the k-AMH algorithm, denoted k-AMH Numeric I, enables it to cluster numerical values in a fashion similar to k-AMH for categorical clustering. The second extension, k-AMH Numeric II, adopts the cost function of the fuzzy k-Means algorithm together with Euclidean distance, and has demonstrated performance to that of k-AMH Numeric I. The clustering performance of the two algorithms was evaluated on six real-world datasets against a benchmark algorithm, the fuzzy k-Means algorithm. The results obtained indicate that the two algorithms are as efficient as the fuzzy k-Means algorithm when clustering numerical values. Further, on an ANOVA test, k-AMH Numeric I obtained the highest accuracy score of 0.69 for the six datasets combined with p-value less than 0.01, indicating a 95% confidence level. The experimental result significantly proved that the k-AMH Numeric I and k-AMH Numeric II can be used for numerical clustering.

Keywords: Cluster analysis, partitional clustering algorithms, categorical and numerical data



Involvement in Knowledge Management Practices among Academicians: case of a MalaysianHigher Learning Institution
1Mohamed Jalaldeen Mohamed Razi, 2Md Habibullah
1, 2Department of Information Systems, International Islamic University Malaysia, Malaysia
razimjm@iium.edu.my ; hadi691@gmail.com

Abstract
Main focus of this study is to investigate the knowledge management (KM) behavior among university academicians in a Malaysian higher learning institutions. Even though many facets of KM have been investigated including in higher learning institutions, more studies are needed as the previous works, especially on higher learning institutions in Malaysia, have focused only on knowledge sharing behavior among academicians. Therefore, this study seeks to investigate academicians’ perceived intention and involvement in KM initiatives and its predictors. A conceptual framework was developed based on the theory of reasoned action and the theory of planned behavior. Intention to be involved in KM and KM behavior were operationalized based on knowledge creation theory through SECI process. Six independent variables representative of the socio cultural nature of KM - trust, management support, decentralization, IT support, performance expectancy, and effort expectancy - were considered as the predictors of KM intention, which in turn, predict KM behavior. Data were collected from 156 academicians from a public university in Malaysia using questionnaires. The questionnaire items were adapted from previous studies. Data were analyzed using SPSS 16 and SmartPLS 3.0. Items with factor loadings less than the threshold value of 0.45 were eliminated from further analysis. The measurement model confirms the Cronbach’s alpha, Composite Reliability, and Average Variance Extracted values were within the acceptable range for most of the constructs, though there were some discrepancies. The discriminant validity was also confirmed. Finally, the structural model analysis confirmed that out of seven proposed hypotheses, four are supported: Trust, performance expectancy, and effort expectancy influence KM Intention, while KM Intention influences KM Behavior. Even though further research works are needed to generalize the findings, the current research and the findings can enrich the KM literature, and provide some insights to the decision makers of the selected higher learning institution on the appropriate KM implementation strategies.

Keywords: knowledge management, Higher Learning Institution, Academicians, Malaysia, SECI



IDENTIFYING SKYLINES IN CLOUD DATABASES WITH INCOMPLETE DATA

1Yonis Gulzar, 2Ali A. Alwan, 3Norsaremah Salleh & 4Imad Fakhri Al Shaikhli
1,2,3&4International Islamic University Malaysia, Malaysia
younisgulzar@gmail.com; aliamer@iium.edu.my; norsaremah@iium.edu.my; imadf@iium.edu.my

Abstract
Skyline queries is a rich area of research in database community. Due to its great benefits, it has been integrated in many database applications including but not limited to personalized recommendation, multi-objective, decision support and decision-making systems. Many variations of skyline technique have been proposed in the literature addressing the issue of handling skyline queries in incomplete database. Nevertheless, these solutions are designed to fit with centralized incomplete database (single access). However, in many real-world database systems, this might not be the case, particularly for a database with large amount of incomplete data distributed over various remote locations such as cloud databases. It is inadequate to directly apply skyline solutions designed for centralized incomplete database to work on cloud due to the prohibitive cost. Thus, this paper introduces a new solution processing skyline queries in incomplete database aiming at minimizing the amount of data transfer and the number of pairwise comparisons that need to be conducted to derive the skyline. The proposed approach adopts a filtration process that attempts to prune the dominated data items before applying skyline process. Filtering the initial database before applying skyline assists in decreasing the number of pairwise comparisons. Most importantly, pruning the dominated data before join helps in reducing the amount of data transfer due to transferring a small portion of the data instead of the transferring the entire database from one datacenter to another. Several experiments have been performed emphasize on evaluating the effectiveness of the proposed approach. The results show that our approach outperforms the previous state-of-the-art approaches in terms of number of pairwise comparisons and the amount of data transferred. We argue that our solution is practical and can be adopted in many contemporary database systems to process skyline queries in an incomplete database.

Keywords: preference queries, skyline queries, algorithms, incomplete data, cloud databases.

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