By Yanchang Zhao
There's frequently a good number of organization principles came across in facts mining perform, making it tricky for clients to spot those who are of specific curiosity to them. for that reason, it is very important get rid of insignificant ideas and prune redundancy in addition to summarize, visualize, and post-mine the found principles.
Post-Mining of organization principles: strategies for potent wisdom Extraction offers a scientific number of examine at the summarization, presentation, and new varieties of organization ideas for post-mining. This ebook provides researchers, practitioners, and academicians with instruments to extract necessary and actionable wisdom after learning various organization principles.
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Additional info for Post-mining of Association Rules: Techniques for Effective Knowledge Extraction
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1998). Beyond Market Baskets: Generalizing Association Rules to Dependence Rules. Data Mining and Knowledge Discovery, 2(1), 39-68. , & Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. KDD ‘02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, 2002, (pp. 32-41). Weiβ, C. H. (2008). Statistical mining of interesting association rules. Statistics and Computing, 18(2), 185-194. 10 Wilhelm, A.
G. in off-the-shelf data mining tools. Examples of such measures are lift, conviction, odds ratio and information gain. Overviews can be found in (Tan and Kumar, 2000), (McGarry, 2005), and (Geng and Hamilton, 2006). , 2004) it is empirically shown that some measures produce similar rankings while others almost reverse the order. This poses the problem of choosing the right measure for a given scenario. One solution is to discover all rules that are interesting to any measure out of a predefined set (Bayardo, Jr.
The different components can be seen as a projection of r and will be referred to as follows: rbody = (r1 ,…, rb ) (5) rhead = (rb +1 ,…, rb + h ) (6) rsym = (r1 ,…, rb + h ) (7) rtime = (rb + h +1 ,…, rb + h + t ) (8) To calculate the item weights ri we adapted the well-known TF-IDF approach (Salton and Buckley, 1987) from information retrieval. The TF-IDF approach weights terms according to their appearance in a document and in the overall document collection. A high term weight, which is correlated with a high importance of that particular term, is achieved if the term appears frequently in the document (term frequency, TF) but much From Change Mining to Relevance Feedback less frequently in the document collection (inverse document frequency, IDF).