CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
International Journal of Computational Science and Engineering
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In this paper we consider the problem of extracting the special properties of any given record in a dataset. We are interested in determining what makes a given record unique or different from the majority of the records in a dataset. In the real world, records typically represent objects or people and it is often worthwhile to know what special properties are present in each object or person, so that we can make the best use of them. This problem has not been considered earlier in the research literature. We approach this problem using ideas from clustering, attribute oriented induction (AOI) and frequent itemset mining. Most of the time consuming work is done in a preprocessing stage and the online computation of the uniqueness of a given record is instantaneous.