151,859 research outputs found
Interactive visual exploration of association rules with rule-focusing methodology
International audienceOn account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm
Interactive Constrained Association Rule Mining
We investigate ways to support interactive mining sessions, in the setting of
association rule mining. In such sessions, users specify conditions (queries)
on the associations to be generated. Our approach is a combination of the
integration of querying conditions inside the mining phase, and the incremental
querying of already generated associations. We present several concrete
algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second
International Conference on Knowledge Discovery and Data Mining (DaWaK 2000
FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases
In recent years, discovery of association rules among itemsets in a large
database has been described as an important database-mining problem. The
problem of discovering association rules has received considerable research
attention and several algorithms for mining frequent itemsets have been
developed. Many algorithms have been proposed to discover rules at single
concept level. However, mining association rules at multiple concept levels may
lead to the discovery of more specific and concrete knowledge from data. The
discovery of multiple level association rules is very much useful in many
applications. In most of the studies for multiple level association rule
mining, the database is scanned repeatedly which affects the efficiency of
mining process. In this research paper, a new method for discovering multilevel
association rules is proposed. It is based on FP-tree structure and uses
cooccurrence frequent item tree to find frequent items in multilevel concept
hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
Set-oriented data mining in relational databases
Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are expressed in terms of relational or set-oriented operations. Query optimization technology can then be used for efficient processing.\ud
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In this paper, we describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and thus may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss optimization of these algorithms. After analytical evaluation, an algorithm named SETM emerges as the algorithm of choice. Algorithm SETM uses only simple database primitives, viz., sorting and merge-scan join. Algorithm SETM is simple, fast, and stable over the range of parameter values. It is easily parallelized and we suggest several additional optimizations. The set-oriented nature of Algorithm SETM makes it possible to develop extensions easily and its performance makes it feasible to build interactive data mining tools for large databases
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