Outlier detection with partial information: application to emergency mapping

Application of microarray outlier detection methodology to psychiatric research

BACKGROUND Most microarray data processing methods negate extreme expression values or alter them so that they do not lie outside the mean level of variation of the system. While microarrays generate a substantial amount of false positive and spurious results, some of the extreme expression values may be valid and could represent true biological findings. METHODS We propose a simple method to...

Application of LVQ to novelty detection using outlier training data

We propose to use learning vector quantization (LVQ) in novelty detection where a few outliers exist in training data. The codebook update of original LVQ is modified and the scheme to determine a threshold for each codebook is proposed. Experimental results on artificial and real-world problems are quite promising.

Outlier Detection Methodologies in Heterogeneous Information Networks

The Partial Weighted Set Cover Problem with Applications to Outlier Detection and Clustering

We define the partial weighted set cover problem, a generic combinatorial optimization problem, that includes some classical data mining problems as special cases. We prove that it is computationally intractable and give a local search algorithm for this problem. As application examples, we then show how to translate clustering and outlier detection problems into this generic problem. Our exper...

Outlier Detection and Clustering by Partial Mixture Modeling

Clustering algorithms based upon nonparametric or semiparametric density estimation are of more theoretical interest than some of the distance-based hierarchical or ad hoc algorithmic procedures. However density estimation is subject to the curse of dimensionality so that care must be exercised. Clustering algorithms are sometimes described as biased since solutions may be highly influenced by ...