Association Rule Mining on Language Trend in MNCs
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 7
Abstract
Data mining system discovers patterns and relationships hidden in data, and actually is a part of a larger process called “knowledge discovery” which describes the steps that must be taken to ensure meaningful results. The presented work will focus on implementing different data mining approaches on database which is simulated from different companies. The work has combined two major approaches to provide profiling of companies and language analysis via clustering and association rules. Once the languages will be identified, then association between segments and profiling of companies will be identified. A comparison between the results which will be obtained from using two different algorithms will be done. One algorithm used is Apriori algorithm and the other one is Simple K Means algorithm which is described in my previous paper. The results obtained from these two algorithms will be identified and compared for the purpose of validation of trends of programming language. All the work is done using WEKA tool. This paper is all about association rule mining to analyse languages in different MNCs.
Authors and Affiliations
Nutan Dahiya, Rachna Dhaka
Keywords
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