Analytical Research on Decision Tree Algorithm and Naive Bayesian Classification Algorithm for Data Mining
Mohammed Mustaqim Rahman, Tarikuzzaman Emon, Zonayed Ahmed · Zenodo (CERN European Organization for Nuclear Research) · 2017
The paper presents an extensive modification of ID3 (Iterative Dichotomiser) algorithm and Naive Bayesian Classification algorithm for data mining. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. This paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today