Abstract :
Over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Data mining algorithms are used extensively to analyze business, commerce, scientific, engineering, and security data and dramatically improve the effectiveness of applications in areas such as marketing, predictive modeling, life sciences, information retrieval, and engineering. The problems of finding these frequent item sets are fundamental in data mining, and from the applications, fast implementations for solving the problems are needed. In this paper, we propose a new, fast and an efficient algorithm with single scan of database for mining complete frequent item sets. Our proposed algorithm works well without any tree construction. At last we performed an experiment on a real dataset to test the run time of our proposed algorithm. The experiment showed that it was efficient for mining dense datasets.
Keywords :
data mining; very large databases; complete frequent item sets; data mining algorithms; dense datasets mining; very large transaction databases; Algorithm design and analysis; Business; Data engineering; Data mining; Data security; Information analysis; Information retrieval; Information security; Predictive models; Transaction databases;
