Browsing by Author "Saheed, Y. K."

Now showing 1 - 3 of 3
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    Comparative Analysis of Association Rule Mining Techniques for Monitoring Behavioural Patterns of Customers in a Grocery Store
    (African Journal of Computing & ICTs (AJOCICT), 2015) Adeniji, I. A.; Saheed, Y. K.; Oladele, Tinuke Omolewa; Braimah, J. O.
    The amount of data being generated and stored is growing exponentially, due to the continuing advances in computer technology. This presents tremendous opportunities for those who can unlock the information embedded within this data, but also introduces new challenges. The primary challenge is how to discover the hidden knowledge or pattern from the large sets of data in order to be able to make intelligent decisions that would shape the future of the store and also to determine the best tool to use in mining. This study proposed to use two different algorithms in association rule mining. The main motivation for carrying out this study is to compare the two algorithms and determine the time complexity of the two algorithms in mining association rules. In this paper, association rule techniques were compared and analyzed for monitoring behavioural pattern of customers in a grocery store. The FP-growth Algorithm and Apriori Algorithm were applied on sixty three (63) datasets from a grocery store. The time complexity of the two algorithms were considered and it was observed that FP-growth Algorithm is more efficient within the resource constraints than Apriori Algorithm. The comparison and analysis were implemented using Weka tool. The results revealed that FP-growth Algorithm is currently one of the fastest approaches for frequent data item set mining.
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    The Relevance of Data Mining Techniques in alleviating Cybersecurity Breaches in Nigeria Healthcare
    (International Journal of Information Processing and Communication (IJIPC), 2018) Ayoade, O. B.; Oladele, Tinuke Omolewa; Saheed, Y. K.
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    STUDENT PERFORMANCE PREDICTION BASED ON DATA MINING CLASSIFICATION TECHNIQUES
    (Nigerian Journal of Technology (NIJOTECH), University of Nsukka., 2018) Saheed, Y. K.; Oladele, Tinuke Omolewa; Akanni, A.O.; Ibrahim, W. M.
    The process of predicting student performance has become a crucial factor in academic environment and plays significant role in producing quality graduates. Several statistical and machine learning algorithms have been proposed for analyzing, predicting and classifying student performance. However, these classification algorithms still posed issue in terms of the performance classification. This paper presents a method to predict student performance using Iterative dichotomiser 3 (ID3), C4.5 and Classification and Regression tree (CART). The experiment was performed on Waikato Environment for Knowledge Analysis (Weka). The experimental results showed that an ID3 accuracy of 95.9% , specificity of 95.9%, precision of 95.9%, recall of 95.9%, f-measure of 95.9% and incorrectly classified instance of 3.83. The C4.5 gave an accuracy of 98.3%, specificity of 98.3%, precision of 98.4%, recall of 98.3%, f-measure of 98.3% and incorrectly classified instance of 1.70. The CART results showed an accuracy of 98.3%, specificity of 98.3%, precision of 98.4%, recall of 98.3%, f-measure of 98.3% and incorrectly classified instance of 1.70. The time taken to build the model of ID3 is 0.05 seconds, C4.5 is 0.03 seconds and CART of 0.58 seconds. Experimental results revealed that C4.5 outperforms other classifiers and requires reasonable amount of time to build the model.