Chan, Chung Hoong and M. A., Ameedeen (2016) Intuitive Content Management System to Preferences Template Throughout Data Collection by using Data Mining Classification of Prediction Method. In: Progress in Computer Sciences and Information Technology International Conference (PROCSIT) , 20 December 2016 , Kedah, Malaysia. pp. 1-4.. (Unpublished)
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Abstract
Data mining is a process to analyse patterns and models for all kinds of data. Data mining involves data management, data pre-processing and metrics. The sophisticated mathematical algorithm applies in data mining to segment data and evaluates the probability of a future event. Many research areas are using data mining which is marketing, mathematics, and genetics. Web mining is a type of data mining which using in customer relation management (CRM). According to information provided, this trend has been increasing year by year and very easy to find those organisations which have store terabytes of data in the database. However, tonnes of data in the database is impossible to mine and fully utilise. In order to solve this issue, therefore Intuitive Content Management System (ICMS) need to use data mining classification to stored data classily, accurately while prediction means to be predictable on database user information to give the best preference and suitable template chosen for an end user to select. The time consuming for choosing a template will be decrease as an example of giving user to choose 5 templates out of 100. As a result, it can minimise the workload of choosing template, time consuming and eyes constraint.
Item Type: | Conference or Workshop Item (Speech) |
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Uncontrolled Keywords: | Data Mining; Classification; Prediction; Intuitive Content Management System |
Subjects: | Q Science > QA Mathematics > QA76 Computer software |
Faculty/Division: | Faculty of Computer System And Software Engineering Centre of Excellence: IBM Centre of Excellence |
Depositing User: | Mrs. Neng Sury Sulaiman |
Date Deposited: | 15 Aug 2017 05:15 |
Last Modified: | 14 Sep 2018 03:44 |
URI: | http://umpir.ump.edu.my/id/eprint/18337 |
Download Statistic: | View Download Statistics |
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