Clustering Approach to Analyse 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. Clustering will be implemented to profiling companies according to the languages. After profiling, work will focuses on finding the trend of programming languages. The system will next identify the most dominant cluster among all the clusters formed.. 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. The results obtained from these two algorithms will be identified and compared for the purpose of validation of trends of programming language. The work has been implemented in Weka Tool. This paper describes the Clustering approach very well.
Authors and Affiliations
Nutan Dahiya, Rachna Dhaka
Keywords
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