data mining research papers 2012 section 5
Multisource causal data mining
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ABSTRACT Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple data sets are sorely
CANCER DIAGNOSIS USING DATA MINING TECHNOLOGY
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ABSTRACT Cancer is a set of diseases in which some cells of the body grow abnormally. These cells then destroy other surrounding cells and their normal functions. Cancer can spread throughout the human body. Since it is a very treacherous disease its diagnosis is
Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach
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Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is
ELF-Miner: Using Structural Knowledge and Data Mining for Detecting Linux Malicious Executables
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Abstract. Linux malware can pose a significant threat–its (Linux) penetration is exponentially increasing–because little is known or understood about its vulnerabilities. We believe that now is the right time to devise non-signature based zero-day (previously unknown)
MADAM ID FOR INTRUSION DETECTION USING DATA MINING
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ABSTRACT Data Mining for IDS is the technique which can be used mainly to identify unknown attacks and to reduce false alarm rates in anomaly detection technique. Various Research Projects using Data Mining techniques for Intrusion Detection are proposed one
Distributed Data Mining for User Sensemaking in Online Collaborative Spaces
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ABSTRACT With the increasing growth of data and knowledge intensive online collaborative spaces, such as blogs, wikis, and discussion forums, making sense of what content exists or which posts are the relevant ones for a user to participate in is becoming more difficult,
A Comparative Analysis of Decision Trees Vis-a-vis Other Computational Data MiningTechniques in Automotive Insurance Fraud Detection
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ABSTRACT The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross- disciplinary research area in recent times involving financial economists, forensic
Future Trend Prediction of Indian IT Stock Market using Association Rule Mining of Transaction data
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1. INTRODUCTION Data Mining also popularly known as Knowledge Discovery in Databases (KDD) refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases.
Performance Analysis of High Performance k-Mean Data Mining Algorithm for Multicore Heterogeneous Compute Cluster
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ABSTRACT In this paper, we have study the performance of k-Mean data-mining algorithm (k-Mean), which is implemented on the heterogeneous compute cluster with the multi core programming. The multicore program is implemented with MPI and C for the parallel
Data Mining Techniques for Real Time Intrusion Detection Systems
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A secure network must provide the following:• Data confidentiality: Data that are being transferred through the network should be accessible only to those that have been properly authorized.• Data integrity: Data should maintain their integrity from the moment they are
Application of Data Mining to Explore Effective Utilization of E-Advertisements in Various Industries
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ABSTRACT E-advertisement data mining can facilitate to identify advertisers and customers buying behaviors, discover customer impressive patterns and trends, improve the quality of e-advertisements, better utilization of e-ads in various industrial sectors, achieve better
Performance Evaluation of Social Network Using Data Mining Techniques
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Social network research relies on a variety of data sources, depending on the problem scenario and the questions, which the research is trying to answer or inform. Social networks are very popular nowadays and the understanding of their inner structure seems to be
Data Mining Techniques for the performance Analysis of a Learning Model–A Case Study
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ABSTRACT This paper deals with a comparative study of the application of various data mining algorithms for the performance analysis of the learning model. The learning model for Mathematics is an integration of the various components used for effective learning of
Predicting the Heart Attack Symptoms using Biomedical Data Mining Techniques
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ABSTRACT The diagnosis of heart disease is a significant and tedious task in medicine. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not mined to determine concealed information for effective decision making by
Data Mining and Social Media
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Mouth Marketing predate the ubiquitous global communications and hyper:connectivity of the Internet age. Fast:forward to 2012, and digital social networks are at the forefront of our social and commercial lives. Internet users are sharing astounding amounts of personal
Preprocessing of Educational Institution Web Log Data for Finding Frequent Patterns using Weighted Association Rule Mining Technique
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Log. Web Log data are massive and erroneous stream data. Handling huge data and incorrect data processing are two significant problems in Web Data Mining. Processing Technique 618 significant challenge in web data mining. Web
Prediction of Course Selection by Student using Combination of Data Mining Algorithms in E-Learning
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ABSTRACT Course recommender system aims at predicting the best combination of courses selected by students. Here in this paper we present how the combination of clustering algorithm-Simple K-means Algorithmassociation rule algorithm-Apriori
Teaching Data Mining in the Business School: Experience from Three Continents
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ABSTRACT The number of data mining courses in Business Schools is growing. Due to the differences in the profiles and interests of business school students compared to students in computer science departments, data mining courses must be organized and delivered in
A Study and Analysis on Cellular Automata based Classifier in Data Mining
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ABSTRACT In the era of Information Technology, information flow has been enormously increased. Data mining techniques are widely used and accepted to retrieve information from various data. Cellular automata based techniques have been extensively reported in
A Five Step Procedure for Outlier Analysis in Data Mining
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ABSTRACT Nowadays, outlier detection is primarily studied as an independent knowledge discovery process merely because outliers might be indicators of interesting events that have never been known before. Despite the advances seen, many issues of outlier
Data Mining-driven Manufacturing Process Optimization
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ABSTRACT High competitive pressure in the global manufac-turing industry makes efficient, effective and continuously improved manufacturing processes a critical success factor. Yet, existing analytics in manufacturing, eg, provided by Manufacturing Execution Systems, are
Use of Data Mining Techniques to Detect Medical Fraud in Health Insurance
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ABSTRACT The health insurance claims application case the inspection usually relies on experts experience for verification and experienced personnel in charge for checking. However, due to the heavy work load and the insufficiency of manpower and experience,
missing attribute values in data mining
A closest fit approach to missing attribute values in data mining
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Abstract Completeness, quality and real world data preparation is a key pre-requisite of successful data mining with its aims to discover something new from the facts already recorded in a certain database. Data preparation for data mining is a fundamental stage of
Rough set approaches to rule induction from incomplete data
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Learning decision tree classifiers from attribute value taxonomies and partially specified data
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AVT-DTL outperforms standard decision tree algorithm (C4.5 and its variants) when applied to data with missingattributevalues; and producesfrom the preference for comprehensible and simple, yet accurate and robust classifiers in many practical applications of datamining.
Imputation of Missing Data Using Machine Learning Techniques.
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Mining with Noise and Missing Data 141To illustrate, let us assume that Autoclass after learning on the training set, classifies a test case x, as belonging to (71 with probability 0.8, and 6'2 with probability of 0.2, based on the non-missingattributevalues of x. If the value of a
Data preprocessing for supervised leaning
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Automated detection of outliers in real-world data
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This is similar to ignoring records containing missing values by some datamining methods.from the fitted value [3]. Actually, if the outlying value is assumed completely erroneous, the correct value can be estimated by any method for estimating missingattributevalues (see [11
An improved comparison of three rough set approaches to missing attribute values
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A Study of K-Nearest Neighbour as an Imputation Method.
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[6] JW Grzymala-Busse and M. Hu. A Comparison of Several Approaches to MissingAttribute Values in DataMining. In Proceedings of the Second International Conference on Rough Sets and Current Trends in Computing RSCTC'2000, pages 340–347, 2000.
A comparative study of classification algorithms for spam email data analysis
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MissingAttributeValues: None Class Distribution: Spam 1813 (39.4%) Non-Spam 2788 (60.6%)Certain datamining techniques algorithm especially ID3 algorithm of decision tree technique require all data to be categorical.
Mining interesting knowledge from data with the XCS classifier system
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Missing attribute values are not considered during covering.From a predictive Data Mining viewpoint, XCSL performs in most cases at least as well as traditional methods while it outperforms C4.5 on an important real-world dataset.
Clustering data without distance functions
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into two clusters A and B. In Figure 3.2, we show some records sampled from partitions A and B. For clarity we show only a few attributes for each data record (we show missingattributevalues byWorkshop on Research Issues on DataMining and Knowledge Discovery, 1997.
A framework to deal with missing data in data sets
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One common problem or challenge in datamining and knowledge discovery research is a noisy data[18].This may result in one or more tuples in the data set conflicting with the Missing attribute values: one or more of the attribute values may be missing both for examples in the
Fuzzy unordered rules induction algorithm used as missing value imputation methods for K-Mean clustering on real cardiovascular data
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I. INTRODUCTION Many real-life data sets are incomplete. The problem with missing attributevalues is a very important issue in DataMining. In medical datamining the problem with the missing values has became a challenging issue.
A Formal Definition of Data Quality Problems.
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Page 2. DQ problems are also labeled of errors, anomalies or even dirtiness and enclose, among others, missingattributevalues, incorrect attribute values, or different representations of the same data. It is not uncommon for
Parimputation: From Imputation and Null-Imputation to Partially Imputation.
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Page 1. Abstract: Missing data imputation is an important step in the process of machine learning and datamining when certain values are missed. AmongB. Research into Missing Data Imputation in DataMining Recently
CHASE2-Rule Based Chase Algorithm for Information Systems of Type lambda.
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A Comparison of several approaches to missingattributevalues in datamining, in Proceedings of the Second International Confer- ence on Rough Sets and Current Trends in Computing,
Visual and automatic data mining for exploration of geographical metadata
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Error detection and impact-sensitive instance ranking in noisy datasets
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For errors introduced by missingattributevalues, the first two steps are trivial, because the instance itself will explicitly indicate whether it contains noise or not (eg, a(2) The Information-gain Ratio (IR) is one of the most popular correlation measures used in datamining.
data mining research papers 2012 section 4
data mining research papers 2012 section 7
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