With the advent of ICT (Information and Communication Technologies) education sector is also experiencing change in teaching process. Different mode of delivery with the use of ICT and digital content has made concept of E-learning and Blended learning more acceptable. But all the available technologies are not used with full potential, sometimes even not introduced at all. Business Intelligence (BI) is one of them. Educational sector also has got vast amount of data scattered in different forms which can be reused to make more intelligent decisions. Various data mining techniques are available which can be used in order to get intelligent information from educational data. Furthermore with the increasing awareness of benefits due to use of Open Source technologies it has become possible for educational institutes to use various technologies with low cost or no cost. In this paper we have used Open Source software Knime for predicting student's results using Naive Bayesian Learner and Naive Bayesian predictor. We also have used Moodle logs data of student's activities as one of the attributes in order to predict results using Naive Bayes theory.

Development of predictive model in education system: using Naïve Bayes classifier

Pervasive application of data mining technology is very important in analytical CRM software development when the distributed data warehouse is constructed. We propose a multi-factor customer classification evaluation model CLV/CL/CC which comprehensively considers customer lifetime value, customer loyalty and customer credit. It classifies clients with synthetic data mining algorithms. In this paper, we present an extended Bayes model which substitutes the primary attribute group with a new attribute group to improve the classification quality of naive Bayes.

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A multi-factor customer classification evaluation model

https://kb2tmp.psu.ac.th/psukb/bitstream/2016/13400/1/435044.pdf

Early Detection of Osteoarthritis Stage by Applying Classification Technique to Human Joint imagery

Naive Bayes classifier, a classification method based on Bayes theory, shows excellent properties in many fields. In practical application, limited to satisfying independence assumption, it is hard to gain better classification. In this paper an simple and intuitive extended Bayes model was constructed, which can get better classification effect. The extended Bayes model replaces primary attribute group with new attribute group (except categorical attribute) and rearrange weights according to the comparison of the expectation of attribute importance, to reinforce the effect of important attributes and weak the effect of subordinate attributes. The extended Bayes model was applied in customer classification prediction and was veried with examples. Firstly, customers were clustered with K-means algorithm, the cluster result as a pretreatment step was used in customer classification. Prediction by weighted Bayes algorithm, it combines the advantages of the two algorithms to improve the accuracy of customer classification. So a customer segmentation model can be built on a basis of customer lifetime value, customer loyalty degree, client capital credit and etc.

Li Wenfeng

Papers

The Research of Customer Classification Based on Extended Bayes Model