123 research outputs found
Monitoring students performance using self organizing map trend clustering
No full textThe analysis of relation between student performance and other variables in education setting is often useful in identifying influential factors on performance. Consequently, the need for adopting an effective tool to process these big data has risen. The analysis of big data will transform passive data into useful information. Data mining is referred to an analytic process designed that discovers data patterns and relationships between datasets. In this study, clustering is used to cluster student grade datasets to generate trend line clusters. The aim of the study is to assist lecturers and academic advisors to recognize the progress of their students
Predictive trend mining for social network analysis
No full textThis thesis describes research work within the theme of trend mining as applied to social network data. Trend mining is a type of temporal data mining that provides observation into how information changes over time. In the context of the work described in this thesis the focus is on how information contained in social networks changes with time. The work described proposes a number of data mining based techniques directed at mechanisms to not only detect change, but also support the analysis of change, with respect to social network data. To this end a trend mining framework is proposed to act as a vehicle for evaluating the ideas presented in this thesis. The framework is called the Predictive Trend Mining Framework (PTMF). It is designed to support "end-to-end" social network trend mining and analysis. The work described in this thesis is divided into two elements: Frequent Pattern Trend Analysis (FPTA) and Prediction Modeling (PM). For evaluation purposes three social network datasets have been considered: Great Britain Cattle Movement, Deeside Insurance and Malaysian Armed Forces Logistic Cargo. The evaluation indicates that a sound mechanism for identifying and analysing trends, and for using this trend knowledge for prediction purposes, has been established
Knowledge management in military: A review for Malaysian Armed Forces’ communities of practices
No full textThe 2nd International Conference on the Roles of Humanities and Social Science in Engineering (ICoHSE 2010) organized by Universiti Malaysia Perlis (UniMAP), 12th - 14th November 2010 at Bayview Beach Resort, Penang, Malaysia.Knowledge management is a process that helps organization to create, identify, manage and
distribute important information including the expertise owned for activities like making
decision, strategic planning, dynamic learning and problem solving. In the business of waging
wars, the military has always maintained that knowledge is critical to victory. For the
military, a learning organization allows its units to gain an advantage over the enemy by
adapting quickly to changing situations and tactics while never remaining in a predictable
pattern. The objective of this paper is to review the management of knowledge in the military
and propose the typology of knowledge that should be codified and shared by the workforce
within the context of empowering the Malaysians Armed Forces with knowledge-based
systems. Communities of Practices (CoP) also will be discussed as to be truly a learning
organization the knowledge must be share from individual to communities
Hybrid Requirement Elicitation Techniques with Lean Six Sigma Methodology for an Enhanced Framework
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Analyzing Network Intrusion Behavior of Packet Capture Using Association Rules Technique: An Initial Framework
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Optimization of software requirement process: An integrated conceptual model of lean six sigma and requirement planning
No full textProviding quality requirements in Software Engineering is vital to ensure the product developed is able to deploy and function to meet the operational objectives. Software Requirement Engineering is the most complex process because it involves the integration of human, logics and process. Extracting or capturing what customers need and want is called Requirement Elicitation (RE) and it is the most crucial process in requirement engineering. If handled poorly, the cost of the failures would be very expensive. Most of the software projects that failed were due to poor requirements which occurred at RE phase. Thus, enhancing and optimizing the RE methods have been subject to a long research debate to ensure quality requirements are captured. Recently, Lean Six Sigma (LSS) had emerged as part of a continuous improvement in Software Development Life Cycles (SDLC). LSS is known for a systematic and structure business improvement successfully deployed in many fields of industry that contributes a significant gain not only in quality of products and services but also in operational costs and delivery. The objective of the research is to develop an integrated conceptual framework of LSS principles with Software Requirement Engineering methodology to optimize RE process. The article will produce conceptual framework as the comprehensive guidelines to capture quality software requirements
Longevity Risk Profiling Based on Non-disease Specific Risk Factors Using Association Rules Mining
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