Fuzzy clustering in Corporate governance

There are many aspects where the shareholders look into a company before, during and after their investment decision. Corporate governance is one of them. The demand for sound corporate governance becomes stronger in recent times to regain public confidence which has been depreciated significantly due to giant corporate failures caused by bad corporate governance practices. Many companies have been practicing corporate transparency (CT). However, good corporate governance (CG) rather than corporate transparency should be a greater concern. Corporate status in adopting sound CG practices may be evaluated either from individual company´s perspective or from a group. Individual achievement has been evaluated through index values in different earlier researches. Current research extends the earlier researches where companies are grouped. It proposes a general Algorithm for developing and deploying automated corporate governance categorizing software system which categorizes the corporate governances system in four groups; namely Excellent, Good, Average and poor based on different Features. Feature analysis is introduced to construct an all-rounded performance variable.