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Research Paper | Computer Science | India | Volume 12 Issue 5, May 2023

Finding PageRank (PR) Using Stochastic Matrix and Multivariate Random Variable

Sreekanth Kavuri | Vedavathi Katneni

Abstract: In the modern world, information is an incredibly important aspect that must not be neglected. In the field of information, several different procedures or algorithms are developed and put to use to extract the required data. Google uses an algorithm called PageRank (PR) to determine where websites should be placed in search engine results. One of the creators of Google, Larry Page, was respected for the naming of the company's ranking algorithm. The relevance of every website on the web is quantified using an algorithm called Page Rank. Many issues in web usage mining have yet to be overcome. The greatest way to quantify anything is via a rating. Because there is an ever-increasing wealth of high-quality information available online. The search engine's effectiveness in yielding useful results has a direct bearing on users' levels of happiness. It becomes more challenging without any help sorting through it all. The issue is that it is not simple to locate the desired information. This study proposes an algorithm for determining the rank of mined web pages that uses a transition matrix and random vector to address these problems. The analysis of the experiment reveals that there were 34 separate iterations of 9 pages, each with a unique PR.

Keywords: Data Mining, Iteration, Random Vectors, Page rank, Markov Chain

Edition: Volume 12 Issue 5, May 2023,

Pages: 975 - 986