data mining research papers 2012-202

data mining research papers 2012-202



big data mining research paper



ART FRAMEWORKS AND PLAT-FORMS FOR PROCESSING AND MANAGING BIG DATA AS WELL AS THE EFFORTS EXPECTED ON BIG DATA MINING
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This paper provides and discussed an overview of big data mining and the related challenges and the new opportunities. Keywords: data mining, big data, big data mining, big data management, knowledge discovery, data-intensive computation. 1. INTRODUCTION

A Debauched Gathering Procedure to Bunch Very Big Definite Figures Sets in Data Mining
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big figures sets, thus very appropriate for figures mining. However, their use is frequently incomplete to numeric figures since these procedures minimize a charge drive by scheming the incomes of clusters. Figures removal presentations frequently include definite data.

European Laboratory on Big Data Analytics and Social Mining
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European Laboratory on Big Data Analytics and Social Mining The SoBigData Lab is a recently created research initiative in Pisa, Italy, with the mission to perform advanced research and analyses on the emerging challenges posed by big data, namely the digital

A Group Mining Method for Big Data on Distributed Vehicle Trajectories in WAN
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In this paper, by integrating the ACA (Ant Colony Algorithm) with the computing framework Map-Combine-Reduce(MCR), a MapReduce based distributed parallel clustering method MCR-ACA is proposed for group mining on big data of ve- hicle trajectories in WAN.

A Survey on Big Data Mining Algorithms
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A Survey on Big Data Mining Algorithms S. Sivasankar1, T. Prabhakaran2Xindong Wu, Xingquan Zhu, Gong-Qing Wu and Wei Ding, Data Mining with Big Data ,

Comparing Business Intelligence and Big Data Skills: A Text Mining Study Using Job Advertisements. BusinessInformation Systems Engineering.
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Comparing Business Intelligence and Big Data Skills: A Text Mining Study Using Job Ad- vertisementsAbstract data mining and machine learning techniques. What differentiates today's big data analytics

Online Big Data Stream Mining Using Adaptive Contexts
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Online Big Data Stream Mining Using Adaptive Contexts We extend the novel coop- erative contextual bandit framework in [5] to learn the best contexts to exploit adaptively over time, and show that this significantly improves the performance of on- line big data mining systems.

Mining Big Data in Real Time.
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Mining Big Data in Real Time Albert Bifet Yahoo! Global Pulse is a United Nations ini- tiative, launched in 2009, that functions as an innovative lab, and that is based in mining Big Data for developing countries.

Big data for education: Data mining, data analytics, and web dashboards
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Big Data for Education: Data Mining, Data Analytics, and Web Dashboards Darrell M. West September 2012 Reuters Page 2. Big Data for Education: Data Mining, Data Analytics, and Web Dashboards 1 EXECUTIVESUMMARY

Big Data Mining by L2 SVMs-Geometrical Insights Help
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Page 1. Big Data Mining by L2 SVMs Geometrical Insights Help ABSTRACT The seminar will introduce a novel learning algorithm for the class of L2 Support Vector Machines classifier dubbed Direct L2 SVM (DL2 SVM). The proposed

Big data mining
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Mining twitter big data to predict
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Mining Twitter Big Data to Election Winner The big data being generated on Twitter daily, and its significant impact on social networking, has motivated the application of data mining (analysis) to extract useful information from tweets.

Technology for mining the big data of MOOCs
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Technology Kalyan Veeramachaneni, Ph.D. Massachusetts Institute of Technology Technology for Mining the Big Data of MOOCs Massive Open Online Courses (MOOCs) are college courses offered on the Internet. Lectures

Reality Mining,(Big Data) and Urban Sensing
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Big Data Mining in the Cloud.
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Big Data and Distributed Data Mining: An Example of Future Networks
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Teaching Data Mining in the Era of Big Data
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Teaching Data Mining in the Era of Big Data Dr. Brian R. King, Bucknell University 5. Data Despite the real-world application of data mining to big data, for pedagogic purposes, the vast majority of topics in data mining are taught using small data sets.

Context-Adaptive Big Data Stream Mining
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Context-Adaptive Big Data Stream Mining Cem Tekin, Luca Canzian, Mihaela van der Schaar Email: mihaela ee.ucla.edu. Fig. 1. Various applications of Big Data stream mining. expected accuracies are unknown a priori to the decision maker/learner.

Big Data in Educational Data Mining and Learning Analytics
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Data mining, Big data, Educational data mining, learning, Learning analytics INTRODUCTION V. CONCLUSION Working with big data using data mining and analytics is rapidly becoming common in the commercial sector.

The Most Advanced Data Mining of the Big Data Era
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The Most Advanced Data Mining of the Big Data Era FUJIMAKI Ryohei, MORINAGA Satoshi ABSTRACT 92 Advanced technologies to support big data processing The Most Advanced Data Mining of the Big Data Era Page 3. As

Discussion about Big Data Mining Based on Hadoop
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Discussion about Big Data Mining based on Hadoop Zhonghua Deng 1, a Bing Fan 2, b YingJun Lu 3, c Zhifang Li 4, d The main aspects of Hadoop for data mining are discussed. A technical framework for big data mining based on Hadoop is analyzed.

Review on Data Mining with Big Data
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REVIEW ARTICLE Review on Data Mining with Big DataIV. Data Mining for Big Data Data mining includes extracting and analyzing bulky amounts of data to discover models for big data.

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCESRESEARCH TECHNOLOGYData Mining: Exploring Big Data Analytics, Hadoop and Mapreduce
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Data mining, Big data, BI, Big Data analytics, OLAP, EDA, Neural Networks, Hadoop and MapReduce technique, Advantages, Disadvantages. Big data analytics found that Apache Hadoop is preferred as solution to the problems in the traditional Data Mining.

Survey of Various Clustering Techniques for Big Data in Data Mining
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In this survey paper, a review of clustering and its different techniques in data mining is done considering the criteria's for big data. Index Terms Clustering Algorithms, Unsupervised learning, Data Mining, Big data. I. INTRODUCTION

A Survey Paper on Data Mining With Big Data
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A Survey Paper on Data Mining With Big Data Rohit Pitre Vijay Kolekar This study paper includes the information about what is big data, Data mining, Data mining with big data, Challenging issues and its related work.

Understanding the patterns in Big Data 'dark matter'with GT data mining
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Understanding the patterns in Big Data 'dark matter' with GT data mining By: Eng. Edith Ohri Codata Conference in Tel-Aviv Even when patterns are visible- Their causes, controlsalerts stay unknown.

Big Data: Mining the Web Data and Its Causes on Retail Marketing
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Big Data: Mining the Web Data and Its Causes on Retail Marketing Figure 2.1: Characteristics of Big Data 3. MINING FREQUENT ITEMS ARE SELECTED BY THE CONSUMERS FROM BIG DATA a. Data storage using HDFS framework: Page 2.

Data Mining: Exploring Big Data Using Hadoop and MapReduce
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Data Mining: Exploring Big Data Using Hadoop and MapReduce M.JAYASREE M.Tech ScholarDept. Keywords: Data Mining, Big data, BI, Big Data analytics, OLAP, EDA, Neural Networks, Hadoop and MapReduce technique, Advantages, Disadvantages.

OLAPing and Mining Big Data: Large-Scale, Long-Running, Serendipitous Computations within Next-Generation Clouds
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OLAPing and Mining Big Data: Large-Scale, Long-Running, Serendipitous Computations within Next-Generation CloudsAbstract. OLAPing and Mining Big Data is among one of the most attracting research contexts of recent years.

AN ANALYSIS THROUGH THE AVAILABLE DATA MINING PROCEDURES WITH BIG DATA
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Issues and Challenges in the Era of Big Data Mining
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This paper provides an overview of big data mining and discusses the related challenges and the new opportunities. Keywords:-data mining, big data, big data mining, knowledge discovery, data-intensive computation. 1. INTRODUCTION

Large-Scale Data Mining and Distributed Processing in Big Data Internet
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Large-scale data mining and distributed processing in big data Internet XING chun-zhi 1,a 1Henan 994 Large-Scale Data Mining and Distributed Processing in Big

Towards a Digital Time Machine fueled by Big Data and Social Mining
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Scientific research is being revolutionized by this new wave, and policy making is next in line, be- cause big data and social mining are providing novel means for measuring and monitoring well-being in our societies more realistically, beyond the GDP, more precisely

Finding Critical Samples for Mining Big Data
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Page 1. Finding Critical Samples for Mining Big Data ABSTRACT “ To ensure success of big data analytics, effective data mining methods are essential; and in mining big data two of the most important problems are sampling and feature selection.

Bio-Inspired Fuzzy Expert system for Mining Big data
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Data Mining and Data Pre-processing for Big Data
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Data Mining Challenges with Big Data [6]: 2012. [8] Xindong Wu, Xingquan Zhu, Gong-Qing Wu, Wei Ding, Data Mining with Big Data , (In Press) IEEE

Temporal Data Mining for Small and Big Data
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Temporal Data Mining Applications: Medicine, bioinformatics, spatiotemporal data C. Temporal Data Mining and Big Data: business process, web data. Page 3. A. General Concepts an area. Page 31. C. Temporal Data Mining and Big Data Page 32. The 3 Vs of Big Data

Islamic Resources Big Data mining, Extraction and Archiving
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Mining œBig Data using Big Data Services
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Uwe Matzat2 Indeed, albeit having peaked in 2001, big data seems to fare well compared to pig data , see Figure 3. But doubts remain. Welcome to the wonderful new world of pig data mining!

Big Data Analytic and Mining with Machine Learning Algorithm
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the Big Data mining techniques should be able to adapt and in some cases to detect change first.

Big Data Mining using Map Reduce: A Survey Paper
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Challenges In Big Data Mining Big Data has different characteristics such as it is large volume, heterogeneous, autonomous source with distributed and centralized control, seek to explore complex and evolving relationship among data .These different characteristics of Big

TOWARDS BIG DATA MINING AND DISCOVERY
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There are also a few reported practical applications of big data mining in the cloud. 1-37. Wu, X., Zhu, X., Wu, G., Ding, W.(2013) Data Mining with Big Data, Knowledge and Data Engineering, IEEE Transactions, in press World Economic Forum (2012).

A Survey on Big Data mining Applications and different Challenges
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Data mining, Big Data, Independent sources, Data sets. So we introduce Big Data mining. II. BIG DATA MINING The Big datais originally found out due to the fact that we are generating a large volume of data each and every day.

A Review: Data Mining for Big Data
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A Review:Data Mining for Big Data Miss. Punde Archana, Miss. Daundkar Kavita, Miss. Shelar Sarswatee To support Big data mining, high- performance computing platforms are required, which impose systematic designs to unleash the full power of the Big Data.

Big Data: The New Challenges in Data Mining
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A Study of Current State of Work and Challenges in Mining Big Data
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feature selection process is used to select a subset of these frequent sub patterns maintaining approximately the same information [7]. Data Mining with Big data had drawn our attention on challenges with mining big data at three levels dealing with data, model, and system.

Big Data: Data Mining Challenges and Related Research
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Big Data mining is the capability of extracting useful information from these large data- sets or streams of data, that due to its volume, variabil- ity, and velocity, it was not possible before to do it.

Mining Association Rules in Big Data for E-healthcare Information System
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Parallelization with Multiplicative Algorithms for big data mining by Birney (2012) suggests that applications of big data mainly focused on the privacy preserving approaches. Xindong, W., Z.

Behavior Mining for Big Data: Promoting Critical Thinking in Data Science Education
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There are also several universal intellectual standards involved in critical thinking for big data mining, including clarity, relevance, depth (which is concerned with how to address the complexities in the question) and breadth (which is concerned with alternative point of views).

Study on the Optimization of Data Mining in Big Data
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Materials Science, Computer and Information Technology Page 5. Materials Science, Computer and Information Technology 10.4028/www.scientific.net/AMR.989-994 Study on the Optimization of Data Mining in Big Data 10.4028/www.scientific.net/AMR.989-994.1837

Mining of Web Server Logs in a Distributed Cluster Using Big Data Technologies
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It is performed using software tools mainly for predictive analysis and data mining. The growing number of technologies is used to aggregate, manipulate, manage and analyze big data. Some of the most prominent technologies are

Smart Cities, Urban Sensing and Big Data: Mining Geo-location in Social Networks
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Smart Cities, Urban Sensing and Big Data: Mining Geo-location in Social Networks Class Description BI similarities Objectives

Digital Footprints: Capturing, Mining, and Contextualizing Big Data
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Digital Footprints: Capturing, Mining, and Contextualizing Big Data Organizer: Digital Footprints

Data mining research papers



FUZZY WEIGHTED ASSOCIATIVE CLASSIFIER: A PREDICTIVE TECHNIQUE FOR HEALTH CARE DATA MINING
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Abstract In this paper we extend the problem of classification using Fuzzy Association Rule Mining and propose the concept of Fuzzy Weighted Associative Classifier (FWAC). Classification based on Association rules is considered to be effective and advantageous

A Multidimensional Model for Automatic Schema Generation process by Integrating OLAP withData Mining Systems
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ABSTRACT Data in a data warehouse is organized in a multidimensional model. This multidimensional model helps in faster query processing and efficient OLAP operations for data analysis and decision making. In this paper, we introduce a framework which

Data Mining: Machine Learning and Statistical Techniques
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The interdisciplinary field of Data Mining (DM) arises from the confluence of statistics and machine learning (artificial intelligence). It provides a technology that helps to analyze and understand the information contained in a database, and it has been used in a large

Data Mining For Selected Clustering Algorithms: A Comparative Study
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ABSTRACT Data mining is the process used to analyze a large amount of heterogeneous data to extract useful information from it. Clustering is one of the main data mining techniques used to divide the data into several groups and each group is called a cluster, which

Modeling of claim counts using data mining procedures in R CRAN
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ABSTRACT. In the ratemaking process the ranking, which takes into account the number of claims generated by a policy in a given period of insurance, may be helpful. For example, such a ranking allows to classify the newly concluded insurance policy to the appropriate

The Non-Negative Matrix Factorization Toolbox for Bio-logical Data Mining
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ABSTRACT Background: Non-negative matrix factorization (NMF) has been being an important approach to analyze biological data. Though there exists some packages implemented in R and other programming languages, they either only provide some optimization algorithms,

Data Mining Techniques in the Diagnosis of Tuberculosis
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Data mining is the knowledge discovery process which helps in extracting interesting patterns from large amount of data. With the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform these data into information.

nwCompare and AutoCompare Softwares for Proteomics and Transcriptomics Data Mining“Application to the Exploration of Gene Expression Profiles of
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The global protein and gene expression profiling technologies have revolutionized the study of normal and malignant cells. Transcriptomes permitted to delineate subtypes of B-cell lymphomas which were otherwise histologically and clinically undistinguishable. Although

A Technical Survey on Cluster Analysis in Data Mining.
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ABSTRACT In data mining functionalities, clustering analysis is the most significant tool for distribution of data. Clustering is dynamic field of research in data mining concept. It is related to unsupervised learning in machine learning. On the basis of similarity measures

Results of Mining Data Features During Computational Fluid Dynamics Simulations
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ABSTRACT Computational Fluid Dynamics (CFD) simula- tions provide a variety of data mining challenges and present an opportunity for novel solutions. A core challenge is that CFD

Tools for Environmental Data Mining and Intelligent Decision Support
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ABSTRACT The joint workshop in Data Mining for Environmental Scientists and Intelligent Environmental Decision Support Systems tries to provide a common discussion forum to communicate environmentalists with data miners and intelligent decision support systems

Overview of the Relational Analysis approach in Data-Mining and Multi-criteria Decision Making
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In this chapter we introduce a general framework called the Relational Analysis approach and its related contributions and applications in the fields of data analysis, data mining and multi-criteria decision making. This approach was initiated by JF Marcotorchino and P.

PREDICTING THE HELPFULNESS OF ONLINE PRODUCT REVIEWERS: A DATA MININGAPPROACH
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ABSTRACT The purpose of this study is to propose a data mining approach to predict the helpfulness scores of online product reviewers. Such prediction can facilitate consumers to judge whether to believe or disbelieve reviews written by different reviewers and can help

Telemetry Data Mining with SVM for Satellite Monitoring
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The aim of this chapter is to provide readers with a basic knowledge of satellite monitoring and data mining for anomaly detection using a support vector machine (SVM) technique. The author describes the design and implementation of an SVM and an example of the

Data Mining in Life Sciences: A Case Study On SAPs In-Memory Computing Engine
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ABSTRACT. While column-oriented in-memory databases have been primarily designed to support fast OLAP queries and business intelligence applications, their analytical performance makes them a promising platform for data mining tasks found in life sciences.

Efficient Data Mining for XML Queries“Answering Support
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ABSTRACT We describe an approach based on Tree-based Association Rules (TARs) mined rules, which provide approximate, intensional information on both the structure and the contents of XML documents and can be stored in XML format as well. There are two main

PREFETCHING NEXT PAGE ACCESS BY ZIPF ESTIMATOR USING DATA MINING TECHNIQUES
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ABSTRACT World Wide Web can be considered as a large distributed information system that provides access to shared data objects. The World Wide Web is of an exponential growth in size, which results in network congestion and server overloading. as more and

Classifying Spyware Files Using Data Mining Algorithms and Hexadecimal Representation
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ABSTRACT Malicious program is a serious threat for the security components such as confidentiality, integrity and availability. These new malicious executables are created at the rate of thousands every year. There are several types of threat to violate these

The TDMR Package: Tuned Data Mining in R
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The TDMR framework is written in R with the aim to facilitate the training, tuning and evaluation of data mining (DM) models. It puts special emphasis on tuning these data mining models as well as simultaneously tuning certain preprocessing options. TDMR is

Neural Network-based Framework for Data Stream Mining
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 Data mining has become the key technique to analyze and understand data.  852“863. Morgan Kaufmann Publishers Inc., Knowledge discovery from data streams, Chapman Hall/CRC Data Mining and Knowledge Discovery Series, (2010).

DETERMINATION STOCK INVESTMENT STRATEGIES OF LISTED COMPANIES IN IRAN USINGDATA MINING TECHNIQUES
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ABSTRACT Investment can be considered as a one of the fundamental pillars of national economy. The single and most important reason is to earn returns on investment. At the present time, many investors look to find a criterion for comparison stock together and

A Survey and Comparative Analysis of Data Mining Techniques for Network Intrusion Detection Systems
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ABSTRACT Despite of growing information technology widely, security has remained one challenging area for computers and networks. In information security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or

Classifying Unstructured Textual Data Using the Product Score Model: An Alternative Text Mining Algorithm
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 exploits information retrieval, information extraction, and corpus-based computational linguistics. Analogous to data mining, text mining seeks to extract useful information from textual data sources by identifying interesting patterns. Page 2.

A Modified Approach to Construct Decision Tree in Data Mining Classification
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ABSTRACT During the late 1970s and early 1980s, J. Ross Quinlan, a researcher in machine learning, developed a decision tree algorithm known as ID3 (Iterative Dichotomiser). This work expanded on earlier work on concept learning systems, described by EB Hunt, J.

SCHOOL AGE CHILDREN S COGNITION IDENTIFICATION BY MINING INTEGRATED COMPUTER GAMES DATA
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strategies. More research is needed to es- tablish to what extent is this approach helpful and in which cases; however our preliminary case stud- ies and data mining method provide the ground work for future studies. Section

Group Movement Pattern Mining Algorithm for Data Compression
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object clustering problem. The temporal-and-spatial correlations in the movements of moving objects are modeled as sequential patterns in data mining to discover the frequent movement patterns. However, sequential patterns

Mining Multi-label Data Streams Using Ensemble-based Active Learning
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Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data
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and many more. Designing algorithms capable of learning from such complex data is one of the most challenging topics of data mining research. This work primarily focuses on developing methods for analyzing electronic health records (EHRs).

Targeted Association Rule Mining in Data Cubes
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their constant support and motivation. i Page 5.Abstract Data mining techniques are usually applied over entire datasets. But the localized behav-  vii Page 11. Chapter 1 Introduction Data mining techniques involve analyzing data to extract interesting patterns




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