Online News Trend Discovery Using Apriori Algorithm
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 6
Abstract
The tremendous growth of Internet has given rise to many news article websites. These news article websites maintain a large amount of information. Discovering usage pattern has direct implication in improving website design, generating advertisements and content distribution. Evaluating popularity prior to release is somewhat desirable, allowing the possibility of reworking the articles and changing the manner of their publication. This task of pattern discovery comes under the broad area of web usage mining. There are a lot of techniques/algorithms that can be employed for web usage mining. In this paper, we discuss the use of Apriori algorithm to identify usage pattern by establishing relationships among various attributes related with a given URL. Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. During this project, we have used association rule mining (Apriori algorithm) to try to discover pattern of popularity on the dataset published by Mashable1, summarizing a heterogeneous set of features about articles published by it in a period of two years.
Authors and Affiliations
Navneet Soni, Dr. S M Ghosh
Keywords
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