Clustering-based web page prediction
International Journal of Knowledge and Web Intelligence
| Hi-index | 0.00 |
In this paper, an approach for storing Markov tree, used in various versions of PPM model while predicting next Web-page is proposed. Markov tree requires huge amount of memory. This problem is solved using Cellular Automata which is considered as a fast and inexpensive mechanism. The proposed technique utilizes non-linear Single Cycle Multiple Attractor Cellular Automata (SMACA) which re- places Markov tree for minimizing the memory requirement. Index Terms - Cellular Automata (CA), Single Cycle Multiple Attractor Cellular Automata (SMACA), Rule Vec- tor (RV), Self Cycle Loop Attractor (SLA), Prediction by Partial Match (PPM), LRS (Longest Repeating Sequence)