Detail Analyzing The Subjective lnterestingness of Association Rules   Oleh:  Liu, Bing ; Hsu, Wynne ; Chen, Shu ; Ma, Yiming Jenis: Article from Bulletin/Magazine Dalam koleksi: IEEE Intelligent Systems vol. 15 no. 5 (2000), page 47-55. Topik: association; subjective interestingness; association rules Ketersediaan Perpustakaan Pusat (Semanggi) Nomor Panggil: II60.4A Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada     Lihat Detail Induk Isi artikel Association rules, a class of important regularities in databases, have proven very useful in practical applications, but association - rule - mining algorithms tend to produce huge numbers of rules, most of which are of no interest. Users have considerable difficulty manually analyzing so many rules to identify the truly interesting ones. To solve that problem, we have developed a new approach to help them find interesting rules (in particular, unexpected rules) from a set of discovered association rules. This interestingness analysis system (IAS) leverages the user's existing domain knowledge to analyze discovered associations and then rank discovered rules according to various interestingness criteria, such as conformity and various types of unexpectedness. This article describes how we have implemented this technique and used it successfully in a number of applications. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Kembali

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