# 2008 FindingNonRedundantStatisticall

- (Moise et al., 2008) ⇒ Gabriela Moise, and Jörg Sander. (2008). “Finding Non-redundant, Statistically Significant Regions in High Dimensional Data: A Novel Approach to Projected and Subspace Clustering.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401956

**Subject Headings:**

## Notes

## Cited By

- http://scholar.google.com/scholar?q=%22Finding+non-redundant%2C+statistically+significant+regions+in+high+dimensional+data%3A+a+novel+approach+to+projected+and+subspace+clustering%22+2008
- http://portal.acm.org/citation.cfm?doid=1401890.1401956&preflayout=flat#citedby

## Quotes

### Author Keywords

### Abstract

Projected and subspace clustering algorithms search for clusters of points in subsets of attributes. Projected clustering computes several disjoint clusters, plus outliers, so that each cluster exists in its own subset of attributes. Subspace clustering enumerates clusters of points in all subsets of attributes, typically producing many overlapping clusters. One problem of existing approaches is that their objectives are stated in a way that is not independent of the particular algorithm proposed to detect such clusters. A second problem is the definition of cluster density based on user-defined parameters, which makes it hard to assess whether the reported clusters are an artifact of the algorithm or whether they actually stand out in the data in a statistical sense.

We propose a novel problem formulation that aims at extracting axis-parallel regions that stand out in the data in a statistical sense. The set of axis-parallel, statistically significant regions that exist in a given data set is typically highly redundant. Therefore, we formulate the problem of representing this set through a reduced, non-redundant set of axis-parallel, statistically significant regions as an optimization problem. Exhaustive search is not a viable solution due to computational infeasibility, and we propose the approximation algorithm STATPC. Our comprehensive experimental evaluation shows that STATPC significantly outperforms existing projected and subspace clustering algorithms in terms of accuracy.

## References

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Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
---|---|---|---|---|---|---|---|---|---|---|

2008 FindingNonRedundantStatisticall | Gabriela Moise Jörg Sander | Finding Non-redundant, Statistically Significant Regions in High Dimensional Data: A Novel Approach to Projected and Subspace Clustering | KDD-2008 Proceedings | 10.1145/1401890.1401956 | 2008 |

Author | Gabriela Moise + and Jörg Sander + |

doi | 10.1145/1401890.1401956 + |

journal | Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining + |

title | Finding Non-redundant, Statistically Significant Regions in High Dimensional Data: A Novel Approach to Projected and Subspace Clustering + |

year | 2008 + |