International Postgraduate Research Conference (IPRC)

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Author: search.filters.author.Liyanage, S.R.Subject: search.filters.subject.Data Mining

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    A Review of Data Mining Methods for Educational Decision Support
    (Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Kasthuriarachchi, K.T.S.; Bhatt, C.M.; Liyanage, S.R.
    Data mining is a computer based information system which is devoted to scanning huge data repositories, generate information and discover knowledge. It attempts to uncover data patterns, organize information of hidden relationships, structure association rules and many more operations that cannot be performed using traditional computer based information systems. Therefore, data mining outcomes represent a valuable support for decisions making in various industries and education is one domain that can benefit from data mining. Application of data mining in education is living in its spring time and preparing for a hot summer season. Educational data mining emerges as a paradigm oriented to design models, tasks, methods, and algorithms for exploring data from educational settings. Educational Data Mining develops and adopts statistical methods, machine- learning and data mining methods to study educational data generated basically by students and educational instructors. The main goal of applying data mining in education is largely to improve learning by enabling data driven decision making for improve current educational practices and learning materials. Educational knowledge discovery, in data mining point of view can be seen as a similar process of applying the general knowledge discovery and data mining process and in experimental point of view, it can be seen as an iterative cycle of hypothesis formation, testing and refinement which not just turn data into knowledge but, also to filter the mined knowledge for decision making. There are many applications in education arena that have been resolved using data mining. There are more research studies have also been conducted under various educational problem categories and also there are a number of frequently used data mining methods use in Educational Data Mining. Various open source and commercial tools are available to apply data mining methods on the educational data. This study focuses on the identification of various educational problem domains where data mining methods can be applied and to study the suitability of the available data mining methods and the tools to perform Educational Data Mining in Sri Lankan Educational Institutes. The knowledge discovered by this review is expected to generate meaningful insight and provide guidance for important decisions made by educators.
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    Sales Prediction with Data Mining Algorithms
    (Faculty of Graduate Studies, University of Kelaniya, 2015) Rathnadiwakara, A.S.K.; Liyanage, S.R.
    Nowadays most business fields using many strategies to improve business profits. Most of them used traditional methods. Therefore, those company‘s efficiency and profit goes to the critical situation. So the improve efficiency of the company is a major requirement for nowadays business platform. Using new technologies companies can improve their profit and efficiency. Also companies can identify their sales life-cycle. This sales prediction was carried for Alfred Edirisinghe (PVT) LTD which is a medium scale tyre dealer in Colombo. Decision Tree, Association Rules and Naïve Bayes data mining models were attempted for the prediction. The best algorithm was selected for each model. Item Code, Item Type, Item Quantity, Item Value, Item Sold Date, etc. variables were used in data mining process. Among those variables five variables were selected for the mining process. A sales data sample with 5000 records were provided by the client for the analysis. Out of the 5000 records 30% was used in the mining process. According to the predicting probabilities, Decision Tree algorithm were performed 98.65%, Association Rules algorithm were performed 100.00% and Naïve Bayes algorithm were performed 99.57%. Decision Tree belongs to the lowest predict probability value. Therefore Decision Tree model was the worst model. Association Rule model contains highest predicted value 100.00%. Therefore it was the best model. Naïve Bayes model was also a good model. The Score results indicate that Decision Trees and Naïve Bayes mining model has the best score 1.00 and followed by Association Rule mining algorithm with score of 0.99. By considering score and target population with predicting probabilities, Association Rule algorithm was the best one for prediction process. Data mining model was implemented using Association Rule algorithm. According to these predicting results, the company can handle their imports optimizing the available resources; storage, time, money. Therefore this research would benefit the Company to improve their incomes.