Performance Evaluation of Clustering Techniques for Financial Crisis Prediction

In the present days, financial crisis prediction (FCP) is winding up progressively in the business advertises. As organizations gather an ever-increasing number of data from day-by-day activities, they hope to draw valuable decisions from the gathered data to aid on practical assessments for new client demands, e.g., client credit classification, certainty of return that was expected, and so forth. Banks as well as institutes of finance have connected diverse mining methods on data to upgrade their business execution. With all these strategies, clustering has been measured as a major strategy to catch the usual organization of data. Be that as it may, there are very few examinations on clustering methodologies for FCP. In this work, we assess two clustering algorithms, namely k-means and farthest first clustering algorithms for parsing distinctive financial datasets shifted out off time periods to trades. The evaluation process was conducted for datasets Weislaw, Polish, and German. The simulation results reported that the k-means clustering algorithm outperforms well than farthest first algorithm on all the applied dataset.