Article history: Received 5 July 2009 Received in revised form 5 May 2010 Accepted 24 May 2010 Available online 4 June 2010 This paper proposes a new kind of data weighted fuzzy c-means clustering approach. Different from most existing fuzzy clustering approaches, the data weighted clustering approach considers the internal connectivity of all data points. An exponent impact factors vector and ...

Local density-based outlier (LOF) is a useful method to detect outliers because of its model free and locally based property. However, the method is very slow for high dimensional datasets. In this paper, we introduce a randomization method that can computer LOF very efficiently for high dimensional datasets. Based on a consistency property of outliers, random points are selected to partition a...

This paper presents an online data-driven algorithm to detect false data injection attacks towards synchronphasor measurements. The proposed algorithm applies density-based local outlier factor (LOF) analysis to detect the anomalies among the data, which can be described as spatiotemporal outliers among all the synchrophasor measurements from the grid. By leveraging the spatio-temporal correlat...

With the increasing use of High-Density Polyethylene (HDPE) piping for nuclear applications, nondestructive evaluation is an important tool for evaluation of the integrity in fused joints. This paper will discuss the method of using Ultrasonic Phased Array for inspecting Butt-Fusion (BF) joints in HDPE piping. The benefit of Phased Array is the ability to perform a volumetric inspection using m...

This paper proposes the method to detect peculiar examples of the target word from a corpus. The peculiar example is regarded as an outlier in the given example set. Therefore we can apply many methods proposed in the data mining domain to our task. In this paper, we propose the method to combine the density based method, Local Outlier Factor (LOF), and One Class SVM, which are representative o...

The conventional collaborative recommendation algorithms are quite vulnerable to user profile injection attacks. To solve this problem, in this paper we propose a robust collaborative recommendation algorithm incorporating trustworthy neighborhood model. Firstly, we present a method to calculate the users’ degree of suspicion based on the user-item ratings data using the theory of entropy and t...

Anomaly detection aims to identify rare events that deviate remarkably from existing data. To satisfy real-world applications, various anomaly detection technologies have been proposed. Due to the resource constraints, such as limited energy, computation ability and memory storage, most of them cannot be directly used in wireless sensor networks (WSNs). In this work, we proposed a hierarchical ...

description systemsF, 112M, 113R, 112V, 112X, 112L, 112Axiomaticsu("), 81(COV0), 86(COV), 81(SSUB), 86Classes of structuresdistanceMS, 15Dd, 15Dm, 15Ds, 15Dt, 15standard framesFd[M], 43Fm[M], 43Fs[M], 43Ft[M], 43LanguagesBoolean modal distanceLOB[M], 33LOB+[M], 36correspondenceLF,...

This paper is an attempt to answer research questions about the performance of foreign banks’ subsidiaries in the U.S. banking environment. The primary focus is to investigate the effects of liability of foreignness on these subsidiaries’ loans portfolio, efficiency, and overall performance by proposing and testing a partial mediating effect of the risky loans portfolio on the relationship betw...

Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to local ...