<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/6340" mdate="2007-01-23 00:00:00"> <author>Nittaya Kerdprasop and Kittisak Kerdprasop</author> <title> Mining Frequent Patterns with Functional Programming</title> <pages>124 - 129</pages> <year>2007</year> <volume>1</volume> <number>1</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/6340</ee> <url>https://publications.waset.org/vol/1</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Frequent patterns are patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a dataset. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages such as C, C&amp;micro;, Java. The imperative paradigm is significantly inefficient when itemset is large and the frequent pattern is long. We suggest a highlevel declarative style of programming using a functional language. Our supposition is that the problem of frequent pattern discovery can be efficiently and concisely implemented via a functional paradigm since pattern matching is a fundamental feature supported by most functional languages. Our frequent pattern mining implementation using the Haskell language confirms our hypothesis about conciseness of the program. The performance studies on speed and memory usage support our intuition on efficiency of functional language. </abstract> <index>Open Science Index 1, 2007</index> </article>