Document Clustering Based on Firefly Algorithm

As energy fuels play a significant role in many parts of human life, it is of great importance to have an effective price predictive analysis. In this chapter, the hybridization of Least Squares Support Vector Machines (LSSVM) with an enhanced Artificial Bee Colony (eABC) is proposed to meet the challenge. The eABC, which serves as an optimization tool for LSSVM, is enhanced by two types of mutations, namely the Levy mutation and the conventional mutation. The Levy mutation is introduced to keep the model from falling into local minimum while the conventional mutation prevents the model from over-fitting and/or under-fitting during learning. Later, the predictive analysis is followed by the LSSVM. Realized in predictive analysis of heating oil prices, the empirical findings not only manifest the superiority of eABC-LSSVM in prediction accuracy but also poses an advantage to escape from premature convergence.

Data Mining is a platform to extract hidden knowledge in a collection of data. This study investigates the suitable classification model to classify graduates employment for one of the MARA Professional College (KPM) in Malaysia. The aim is to classify the graduates into either as employed, unemployed or further study. Five data mining algorithms offered in WEKA were used; Naïve Bayes, Logistic regression, Multilayer perceptron, k-nearest neighbor and Decision tree J48. Based on the obtained result, it is learned that the Logistic regression produces the highest classification accuracy which is at 92.5%. Such result was obtained while using 80% data for training and 20% for testing. The produced classification model will benefit the management of the college as it provides insight to the quality of graduates that they produce and how their curriculum can be improved to cater the needs from the industry.