Density estimation methods are often regarded as unsuitable for anomaly detection in high-dimensional data due to the difficulty of estimating multivariate probability distributions. Instead, the scores from popular distanceand localdensity-based methods, such as local outlier factor (LOF), are used as surrogates for probability densities. We question this infeasibility assumption and explore a...

 Clopidogrel is recommended as part of dual antiplatelet therapy (DAT) for clot prevention in patients with acute coronary syndrome (ACS). Clopidogrel is a prodrug that requires activation by CYP2C19 enzymes in the liver. It is expected that 16% to 50% of clopidogrel-treated patients who have ACS may not fully benefit from the protective effect of clopidogrel, potentially remaining at an incre...

Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in sc...

Detecting outliers in mixed attribute datasets is one of major challenges in real world applications. Existing outlier detection methods lack effectiveness for mixed attribute datasets mainly due to their inability of considering interactions among different types of, e.g., numerical and categorical attributes. To address this issue in mixed attribute datasets, we propose a novel Pattern based ...

Many water boiler manufacturers are not able to detect the occurrence of failures in the machines they produce before they can pose inconvenience and sometimes danger for costumers and workers. Moreover, the number of boilers that have to be monitored, are many times in the range of the thousands or even millions, proportionaly to the number of costumers a company possesses. The detection of th...

For natural language processing on machines, resolving such peculiar usages would be particularly useful in constructing a dictionary and dataset for word sense disambiguation. Hence, it is necessary to develop a method to detect such peculiar examples of a target word from a corpus. Note that, hereinafter, we define a peculiar example as an instance in which the target word or phrase has a new...

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Densitybased Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of...

As one of data mining techniques, outlier detection aims to discover outlying observations that deviate substantially from the reminder of the data. Recently, the Local Outlier Factor (LOF) algorithm has been successfully applied to outlier detection. However, due to the computational complexity of the LOF algorithm, its application to large data with high dimension has been limited. The aim of...

Changes in the measured structural responses, such as deflections and modal properties, induced by damage could be significantly smaller than those by environmental effects such as temperature and temperature gradients. To make the structural health monitoring more reliable and applicable to real structures, it is highly desirable to develop a methodology to distinguish the changes due to the s...

This paper investigates the use of an unsupervised hybrid statistical–local outlier factor algorithm to detect anomalies in time-series flight data. Flight data analysis is activity carried out by airlines primarily as a means improving safety and operation their fleet. Traditionally, this performed checking exceedances pre-set limits parameters. However, method highlights single events during ...