Data Mining

Page 1. An Intelligent Data M iner tool for a Publication database B. SivaSelvan Research Scholar, NIT Tiruchirapalli-15. siva.selvan@gmail.com NP Gopalan Professor, NIT Tiruchirapalli-15.gopalan@nitt.edu K. Batri Research Scholar, NIT Tiruchirapalli-15. ...

Essential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects. We found that cortical thickness features alone distinguished the two, ET from controls, with 81% diagnostic accuracy. More specifically, roughness (i.e., the standard deviation of cortical thickness) of the right inferior parietal and right fusiform areas was shown to play a key role in ET characterization. Moreover, these features allowed us to identify subgroups of ET patients as well as healthy subjects at risk for ET...

To develop a system for the automatic classification of Cancer Registry notifications data from free-text pathology reports. The underlying technology used for the extraction of cancer notification items is based on the symbolic rule-based classification methodology, whereby formal semantics are used to reason with the systematised nomenclature of medicine - clinical terms (SNOMED CT) concepts identified in the free text. Business rules for cancer notifications used by Cancer Registry coding staff were also incorporated with the aim to mimic Cancer Registry processes. The system was developed on a corpus of 239 histology and cytology reports (with 60% notifiable reports), and then evaluated on an independent set of 300 reports (with 20% notifiable reports). Results show that the system can reliably classify notifiable reports with 96% and 100% specificity, and achieve an overall accuracy of 82% and 74% for classifying notification items from notifiable reports at a unit record level...

To develop a system for the automatic classification of Cancer Registry notifications data from free-text pathology reports. The underlying technology used for the extraction of cancer notification items is based on the symbolic rule-based classification methodology, whereby formal semantics are used to reason with the systematised nomenclature of medicine - clinical terms (SNOMED CT) concepts identified in the free text. Business rules for cancer notifications used by Cancer Registry coding staff were also incorporated with the aim to mimic Cancer Registry processes. The system was developed on a corpus of 239 histology and cytology reports (with 60% notifiable reports), and then evaluated on an independent set of 300 reports (with 20% notifiable reports). Results show that the system can reliably classify notifiable reports with 96% and 100% specificity, and achieve an overall accuracy of 82% and 74% for classifying notification items from notifiable reports at a unit record level from the development and evaluation set, respectively. Cancer Registries collect a multitude of data that requires manual review, slowing down the flow of information. Extracting and providing an automatically coded cancer pathology notification for review can lessen the reliance on expert clinical staff, improving the efficiency and availability of cancer information.

Oxidative stress generating active oxygen species has been proved to be one of the underlying agents causing tissue injury after the exposure of Eucalyptus (Eucalyptus spp.) plants to a wide variety of stress conditions. The objective of this study was to perform data mining to identify favorable genes and alleles associated with the enzyme systems superoxide dismutase, catalase, peroxidases, and glutathione S-transferase that are related to tolerance for environmental stresses and damage caused by pests, diseases, herbicides, and by weeds themselves. This was undertaken by using the eucalyptus expressed-sequence database (https//forests.esalq.usp.br). The alignment results between amino acid and nucleotide sequences indicated that the studied enzymes were adequately represented in the ESTs database of the FORESTs project.

GECAD – Knowledge Engineering and Decision Support Group Institute of Engineering, Polytechnic of Porto, Portugal E-mail: {viamonte,csr,fr}@dei.isep.ipp.pt ... Department of Electrical Engineering, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal E-mail: ...

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customers' behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client's database. To form the different customers' classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

The aim of this research is to develop a fusion concept to component-based face recognition algorithms for features analysis of binary facial components (BFCs), which are invariant to illumination, expression, pose variations and partial occlusion. To analyze the features, using statistical pattern matching concepts, which are the combination of Chi-square (CSQ), Hu moment invariants (HuMIs), absolute difference probability of white pixels (AbsDifPWPs) and geometric distance values (GDVs) have been proposed for face recognition. The individual grayscale face image is cropped by applying the Viola–Jones face detection algorithm from a face database having variations in illumination, appearance, pose and partial occlusion with complex backgrounds. Doing illumination correction through histogram linearization technique, the grayscale face components such as eyes, nose and mouth regions are extracted using the 2D geometric positions. The binary face image is created by applying cumulati...

Today, with the advent of internet, everyone can do information exchange and resource sharing. Even business organization and government agencies are not behind in this move to reach users for their decision making and for business strategies. But at the same time, with ease of use and availability of various software tools, breaching and penetrating into other‟s network and confidential credential can be done by any individual with little knowledge expertise and hence the internet attacks are rise and are main concerns for all internet users and business organizations for internal as well as external intruders. Even, existing solutions and commercial Intrusion Detection Systems (IDSs) are developed with limited and specific intrusion attack detection capabilities without any prevention capabilities to secure vital resources of the information infrastructure. So, this paper explores the details about the implementation and experimental analysis of Advanced Intrusion Detection System...

This paper summarizes the results obtained from the application of Data Mining techniques in order to detect usual behaviors in the use of computers. For that, based on real security event logs, two different clustering strategies have been developed. On the one hand, a clustering process has been carried out taking into account the characteristics that define the events in a quantitative way. On the other hand, an approach based on qualitative aspects has been developed, mainly based on the interruptions among security events. Both approaches have shown to be effective and complementary in order to cluster security audit trails of Windows systems and extract useful behavior patterns.

Motion analysis studies provide efficient estimation schemes for estimating dense optical flow fields. However, for image sequence coding where motion features have to be transmitted, an adaptive compact motion representation is required to extract temporal redundancy from an image sequence with a minimum amount of side information and, nevertheless, with a high quality of reconstruction. The advantages of region-based motion descriptions are illustrated and compared to those of the usual local ones (i.e., only two purely translational components of apparent motion). A new relaxation method for compact quantitative motion estimation using a hierarchy of motion models is presented

Motion analysis studies provide efficient estimation schemes for estimating dense optical flow fields. However, for image sequence coding where motion features have to be transmitted, an adaptive compact motion representation is required to extract temporal redundancy from an image sequence with a minimum amount of side information and, nevertheless, with a high quality of reconstruction. The advantages of region-based motion descriptions are illustrated and compared to those of the usual local ones (i.e., only two purely translational components of apparent motion). A new relaxation method for compact quantitative motion estimation using a hierarchy of motion models is presented