Big Data contains massive information, which are generating from heterogeneous, autonomous sources with distributed and anonymous platforms. Since, it raises extreme challenge to organizations to store and process these data. Conventional pathway of store and process is happening as collection of manual steps and it is consuming various resources. An automated real-time and online analytical pr...

Existing Data Mining process models propose one way or another of developing projects in a structured manner, trying to reduce their complexity through effective project management. It is well-known in any engineering environment that one of the management tasks that helps to reduce project problems is systematic project documentation, but few of the existing Data Mining processes propose their...

The CRISP-DM (CRoss Industry Standard Process for Data Mining) project proposed a comprehensive process model for carrying out data mining projects. The process model is independent of both the industry sector and the technology used. In this paper we argue in favor of a standard process model for data mining and report some experiences with the CRISP-DM process model in practice. We applied an...

Alert management plays a critical role in many application domains including homeland security and natural disaster management, to allow timely and well-informed decisions. The major challenge faced by these systems is that the number of incoming alarms is overwhelming and some of the alarms are false positives. In this paper, we present an alert management system (AMS) that generates meaningfu...

Internet banking has become widely available in Jamaica and yet there have been few studies to understand the characteristics of its users. For banks to improve their service and similar services it becomes imperative that they can justify the costs associated with these services. One of the ways these costs can be justified is if their customer base of internet banking users was to increase, t...

This research report is written for attendance of DMC 2002. (See [8]) It was written following the CRISP-DM (CRISPData Mining) [1] Methodology: Business understanding, Data Understanding, Data Preparation, Modeling and Evaluation. Two popular data mining software products are used: DISCOVERER is mainly used for data preparation and modeling. WEKA is used to feature selection. At the last part o...

This study clusters customers and finds the characteristics of different groups in a life insurance company in order to find a way for prediction of customer behavior based on payment. The approach is to use clustering and association rules based on CRISP-DM methodology in data mining. The researcher could classify customers of each policy in three different clusters, using association rules. A...

Mounting amounts of data made traditional data analysis methods impractical. Data mining (DM) tools provide a useful for alternative framework that addresses this problem. This study follows a DM technique to identify diabetic patients. We develop a model that clusters diabetes patients of a large healthcare company into different subpopulation. Consequently, we show the value of applying a DM ...

Mounting amounts of data made traditional data analysis methods impractical. Data mining (DM) tools provide a useful for alternative framework that addresses this problem. This study follows a DM technique to identify diabetic patients. We develop a model that clusters diabetes patients of a large healthcare company into different subpopulation. Consequently, we show the value of applying a DM ...

Data Mining(DM) technique is used to mine interesting hidden knowledge from large databases using various computational techniques/ tools. Association Rule Mining(ARM) today is one of the most important aspects of DM tasks. In ARM all the strong association rules are generated from the Frequent Itemsets. In this study a central Data Warehouse based client-server model for ARM is designed, imple...