Efficiency stress prediction in BPO industries using hybrid k-means and artificial bee colony algorithm

ABSTRACT

The exploration of business process outsourcing (BPO) is a huge attention in India. A concern, the performance outcomes, and hope of Indian service workers are efficient. To motivate the employee developing and managing levels of their personal and business level, and also their problems such as erosion, pressure, and burnout that have afflicted the BPO industry. The main motivation is to reduce the stress level of employees in BPO field with data mining techniques. The data mining is to demonstrate the potential of gathering large collections of data and analyzing these datasets to gain useful business-oriented information. In this paper, for all the above algorithms, the input dataset will be considered as employees issues. Based on the given input, each algorithm will be processed, and the respective solution is obtained. As per the performance of the given algorithm is not that much predictive in the accuracy and high error rate, we propose a new method called hybrid k-means and artificial bee colony algorithm which will enhance accuracy rate and reduce the error rate.

KEYWORDS:

• Text mining
• k-means clustering
• artificial bee colony
• BPO industries
• stress issues