FUZZY HIDDEN MARKOV CHAIN FOR WEB APPLICATIONS

Hidden Markov model (HMM) has become increasingly popular in the last several years. Real-world problems such as prediction of web navigation are uncertain in nature; in this case, HMM is less appropriate i.e., we cannot assign certain probability values while in fuzzy set theory everything has elasticity. In addition to that, a theory of possibility on fuzzy sets has been developed to handle uncertainity. Thus, we propose a fuzzy hidden Markov chain (FHMC) on possibility space and solve three basic problems of classical HMM in our proposed model to overcome the ambiguous situation. Client's browsing behavior is an interesting aspect in web access. Analysis of this issue can be of great benefit in discovering user's behavior in this way we have applied our proposed model to our institution's website () to identify how well a given model matches a given observation sequence, next to find the corresponding state sequence which is the best to explain the given observation sequence and then to attempt to optimize the model parameters so as to describe best how a given observation sequence comes about. The solution of these problems help us to know the authenticity of the website.