ABSTRACT For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend

, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as \How many clusters are there?", "Which clustering method should be used?" and \How should outliers be handled?". We outline a general methodology

that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "

Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Detection of spatial outliers helps reveal valuable information from large spatial data sets. In many real applications, spatial objects can not be simply abstracted as isolated points. They have

Abstract The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly

to be extracted quickly and accurately using simple gesture motions with a mouse. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary “snaps ” to, and wraps around the object of interest. Live-wire boundary detection formulates discrete dynamic programming (DP) as a two

We propose a measure, Spatial Local Outlier Measure (SLOM) which captures the local behaviour of datum in their spatial neighborhood. With the help of SLOM we are able to discern local spatial outliers which are usually missed by global techniques like “three standard deviations away from the mean

Outlier detection is an important problem that has been researched within diverse knowledge disciplines and application domains. Many of these techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured

, and many other forms of network analysis. Only recently has there been some work in the area of outlier detection for information network data. Outlier (or anomaly) detection is a very broad field and has been studied in the context of a large number of application domains. Many algorithms have been

should be efficient and detect clusters of arbitrary shape. It must be insensitive to the outliers (noise) and the order of input data. We pro-pose WaveCluster, a novel clustering approach based on wavelet transforms, which satisfies all the above requirements. Using multi-resolution property of wavelet