Anomaly detection is an important problem that has been popularly researched within diverse research areas and application domains. One of the open problems in anomaly detection is the modeling and prediction of complex sequential data, which consist of a series of temporally related behavior patterns. In this paper, a novel sequential anomaly detection method based on temporal-difference (TD) ...

We present a method for automatically detecting errors in a manually marked corpus using anomaly detection. Anomaly detection is a method for determining which elements of a large data set do not conform to the whole. This method fits a probability distribution over the data and applies a statistical test to detect anomalous elements. In the corpus error detection problem, anomalous elements ar...

(The paper appears in its original form on the next page.) @inproceedings{CJR-MIUA-2004, author = {C. abstract = {We present a generative parametric statistical model of the appearance of entire digital x-ray mammograms. Computer-aided detection (CADe) in mammography has traditionally been treated as a pattern recognition task, where an attempt is made to emulate radiologists' interpretation st...

The research to data streaming model has recently gained a high attraction due to its applications, including real-time surveillance systems, network intrusion detection and click streams. Clustering, one of the most important problems in streaming mining, has recently been highly explored because its application to data summarization and outlier detection. Due to the characteristics of data st...

Clustering and outlier detection are important data mining areas. Online clustering and outlier detection generally work with continuous data streams generated at a rapid rate and have many practical applications, such as network instruction detection and online fraud detection. This chapter first reviews related background of online clustering and outlier detection. Then, an incremental cluste...

Plagiarism detection has been considered as a classification problem which can be approximated with intrinsic strategies, considering self-based information from a given document, and external strategies, considering comparison techniques between a suspicious document and different sources. In this work, both intrinsic and external approaches for plagiarism detection are presented. First, the m...

Algorithms for anomaly detection in IP networks have been developed and a real-time distributed platform for anomaly detection has been implemented. These algorithms automatically and adaptively detect “soft” network faults (performance degradations) in IP networks. These algorithms are implemented as a reliable and fully distributed real-time software platform called NSAD (Network/Service Anom...

Techniques for video object motion analysis, behaviour recognition and event detection are becoming increasingly important with the rapid increase in demand for and deployment of video surveillance systems. Motion trajectories provide rich spatiotemporal information about an object’s activity. This paper presents a novel technique for classification of motion activity and anomaly detection usin...

Data mining methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. It is a processing procedure of extracting credible, novel, effective and understandable patterns from database. This paper is used to categorize the students into grade order in all their education studies and it helps ...

We show how the coupling of gravitinos and gauginos to fluxes modifies anomaly cancellation in M-theory on a manifold with boundary. Anomaly cancellation continues to hold, after a shift of the definition of the gauge currents by a local gauge invariant expression in the curvatures and E 8 fieldstrengths. We compute the first nontrivial correction of