## Ranking the Interestingness of Summaries from Data Mining Systems (1999)

Venue: | In Proceedings of the 12th Annual Florida Artificial Intelligence Research Symposium (FLAIRS'99 |

Citations: | 6 - 3 self |

### BibTeX

@INPROCEEDINGS{Hilderman99rankingthe,

author = {Robert J. Hilderman and Howard J. Hamilton and Brock Barber},

title = {Ranking the Interestingness of Summaries from Data Mining Systems},

booktitle = {In Proceedings of the 12th Annual Florida Artificial Intelligence Research Symposium (FLAIRS'99},

year = {1999},

pages = {100--106}

}

### Years of Citing Articles

### OpenURL

### Abstract

We study data mining where the task is description by summarization, the representation language is generalized relations, the evaluation criteria are based on heuristic measures of interestingness, and the method for searching is the Multi-Attribute Generalization algorithm for domain generalization graphs. We present and empirically compare four heuristics for ranking the interestingness of generalized relations (or summaries). The measures are based on common measures of the diversity of a population, statistical variance, the Simpson index, and the Shannon index. All four measures rank less complex summaries (i.e., those with few tuples and/or non-ANY attributes) as most interesting. Highly ranked summaries provide a reasonable starting point for further analysis of discovered knowledge.

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Citation Context ...imited in their ability to efficiently generate summaries when multiple CHs were associated with an attribute. To resolve this problem, we previously introduced new serial and parallel AOG algorithms =-=[12; 16]-=- and a data structure called a domain generalization graph (DGG) [12; 13; 16; 21]. A DGG for an attribute is a directed graph where each node represents a domain of values created by partitioning the ... |

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Citation Context ...ons, the evaluation criteria are based on heuristic measures of interestingness, and the method for searching is the Multi-Attribute Generalization algorithm [12] for domain generalization graphs. In =-=[15]-=-, we proposed four heuristics, based upon information theory and statistics, for ranking the interestingness of summaries generated from a database. Preliminary results suggested that the order in whi... |

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Citation Context ...red knowledge via the explicit detection of occurrences of Simpson's paradox. Finally, an excellent survey of informationtheoretic measures for evaluating the importance of attributes is described in =-=[26]-=-. Although our measures were developed and utilized for ranking the interestingness of generalized relations as described earlier in this section, they are more generally applicable to other problem d... |

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Citation Context ... 0.11935 22 Experimental Results In this section, we present experimental results which contrast the various interestingness measures. All summaries in our experiments were generated using DBDiscover =-=[5; 6]-=-, a software tool which uses AOG for KDD. DB-Discover was run on a Silicon Graphics Challenge M, with twelve 150 MHz MIPS R4400 CPUs, using Oracle Release 7.3 for database management. Description of D... |

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