Personality and Education Mining based Job Advisory System

Every job demands an employee with some specific qualities in addition to the basic educational qualification. For example, an introvert person cannot be a good leader despite of a very good academic qualification. Thinking and logical ability is required for a person to be a successful software engineer. So, the aim of this paper is to present a novel approach for advising an ideal job to the job seeker while considering his personality trait and educational qualification both. Very well-known theories of personality like MBTI indicator and OCEAN theory, are used for personality mining. For education mining, score based system is used. The score based system captures the information from attributes like most scoring subject, dream job etc. After personality mining, the resultant values are coalesced with the information extracted from education mining. And finally, the most suited jobs, in terms of personality and educational qualification are recommended to the job seekers. The experiment is conducted on the students who have earned an engineering degree in the field of computer science, information technology and electronics. Nevertheless, the same architecture can easily be extended to other educational degrees also. To the best of the author’s knowledge, this is a first e-job advisory system that recommends the job best suited as per one’s personality using MBTI and OCEAN theory both

Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering

The fuzzy clustering algorithm is a partition method that assigns objects from a data set to a cluster by marking the average location. Furthermore, Fuzzy Subtractive Clustering (FSC) with hamming distance and exponential membership function is used to analyze the cluster center of a data point. The data point with the highest density will be the cluster's center. Therefore, this research aims to determine the number of collections with the best quality by comparing the Partition Coefficient (PC) values for each number produced. The data set, which is heart failure patient data, is 150 data obtained from UCI Machine Learning. The data consists of 11 variables, including age