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Abstract. Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2,11]. However, when outliers are in the location where the density distributions in the neighborhood are significantly different, for example, in the case of objects from a sparse cluster close to a denser cluster, this may result in wrong estimation. To avoid this problem, here we propose a simple but effective measure on local outliers based on a symmetric neighborhood relationship. The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. To compute such local outliers efficiently, several mining algorithms are developed that detects top-n outliers based on our definition. A comprehensive performance evaluation and analysis shows that our methods are not only efficient in the computation but also more effective in ranking outliers.

Abstract. In many decision-making applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset. In a high-dimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness of data points based on how often they are returned in the skyline when different number of dimensions (i.e., subspaces) are considered. Intuitively, a point with a high skyline frequency is more interesting as it can be dominated on fewer combinations of the dimensions. Thus, the problem becomes one of finding top-k frequent skyline points. But the algorithms thus far proposed for skyline computation typically do not scale well with dimensionality. Moreover, frequent skyline computation requires that skylines be computed for each of an exponential number of subsets of the dimensions. We present efficient approximate algorithms to address these twin difficulties. Our extensive performance study shows that our approximate algorithm can run fast and compute the correct result on large data sets in high-dimensional spaces.

In this paper, we propose a novel algorithm to discover the topk covering rule groups for each row of gene expression profiles. Several experiments on real bioinformatics datasets show that the new top-k covering rule mining algorithm is orders of magnitude faster than previous association rule mining algorithms.Furthermore, we propose a new classification method RCBT. RCBT classifier is constructed from the top-k covering rule groups. The rule groups generated for building RCBT are bounded in number. This is in contrast to existing rule-based classification methods like CBA [19] which despite generating excessive number of redundant rules, is still unable to cover some training data with the discovered rules. Experiments show that the RCBT classifier can match or outperform other state-of-the-art classifiers on several benchmark gene expression datasets. In addition, the top-k covering rule groups themselves provide insights into the mechanisms responsible for diseases directly.

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Abstract-Mapping mashups are emerging Web 2.0 applications in which data objects such as blogs, photos and videos from different sources are combined and marked in a map using APIs that are released by online mapping solutions such as Google and Yahoo Maps. These objects are typically associated with a set of tags capturing the embedded semantic and a set of coordinates indicating their geographical locations. Traditional web resource searching strategies are not effective in such an environment due to the lack of the gazetteer context in the tags. Instead, a better alternative approach is to locate an object by tag matching. However, the number of tags associated with each object is typically small, making it difficult for an object to capture the complete semantics in the query objects.In this paper, we focus on the fundamental application of locating geographical resources and propose an efficient tagcentric query processing strategy. In particular, we aim to find a set of nearest co-located objects which together match the query tags. Given the fact that there could be large number of data objects and tags, we develop an efficient search algorithm that can scale up in terms of the number of objects and tags. Further, to ensure that the results are relevant, we also propose a geographical context sensitive geo-tf-idf ranking mechanism. Our experiments on synthetic data sets demonstrate its scalability while the experiments using the real life data set confirm its practicality.

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