data mining research papers 2012
Data Mining and warehousing for Comprehensive Web Based Project Management Software.
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MF Khalil, HS Acharya, M Alam ,2012 ,jctjournals.com
Abstract-The project Management Software will be used to collect the information from the
different levels of software development and different people involved in the project and
product development. It provides Status and Reports of different levels of project and
The Survey of Data Mining Applications And Feature Scope
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N Padhy, P Mishra, R Panigrahi ,International Journal of Computer , 2012
Abstract In this paper we have focused a variety of techniques, approaches and different
areas of the research which are helpful and marked as the important field of data mining
Technologies. As we are aware that many MNC’s and large organizations are operated in
An Introduction to SIGKDD and A Reflection on the Term ‘Data Mining’
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G Piatetsky-Shapiro, U Fayyad ,ACM SIGKDD Explorations Newsletter, 2012 ,kdd.org
We live in the age of Big Data-the Second major revolution in modern civilization after the
Industrial revolution: the Information Revolution. Big data is used to improve sales, reduce
customer attrition, recommend movies, music, and people, reduce fraud, predict crime,
A Novel Rule based Data Mining Mechanism for Identification of Mutual Relationship among Item Sets
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S Arya, RP Agarwal ,Age, 2012
ABSTRACT In recent days, there is more interest in mutually dependent item sets rather than
frequent pattern sets for applications in specific domains viz. identification of irregularities in
stock marketing, assessing the causes in certain diseases, identifying irregularities in
Integrating Data Mining in Program Evaluation of K-12 Online Education
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ABSTRACT This study investigated an innovative approach of program evaluation through
analyses of student learning logs, demographic data, and end-of-course evaluation surveys
in an online K–12 supplemental program. The results support the development of a
A Comparative Study of Various Clustering Algorithms in Data Mining
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M Verma, M Srivastava, N Chack, AK Diswar ,International Journal of , 2012
Abstract-Data clustering is a process of putting similar data into groups. A clustering
algorithm partitions a data set into several groups such that the similarity within a group is
larger than among groups. This paper reviews six types of clustering techniques-k-Means
Temporal Data Mining of Scientific Data Provenance
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Abstract. Provenance of digital scientific data is an important piece of the metadata of a data
object. It can however grow voluminous quickly because the granularity level of capture can
be high. It can also be quite feature rich. We propose a representation of the provenance
Data Mining Techniques in Clustering, Association and Classification
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DE Holmes, J Tweedale, LC Jain Data Mining: Foundations and , 2012 ,Springer
The term Data Mining grew from the relentless growth of techniques used to interrogation
masses of data. As a myriad of databases emanated from disparate industries, management
insisted their information officers develop methodology to exploit the knowledge held in
Data Mining Methods for ‘OMICs’ Applications in Anticancer Drug Design and Discovery
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S Sardari ,Drug Design, 2012 ,omicsgroup.org
Cancer forces a serious burden on the public health system and has created a challenge to
the medical science researchers. Though the century-long drift of cancer mortality in the
world was reversed in the middle of 19th century and now cancer remains the second
Fuzzy Rules in Data Mining: From Fuzzy Associations to Gradual Dependencies
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Fuzzy rules, doubtlessly one of the most powerful tools of fuzzy logic, have not only been
used successfully in established application areas like control engineering and approximate
reasoning, but more recently also in the field of data mining. In this chapter, we provide a
Experiences and lessons in developing industry-strength machine learning and data miningsoftware
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In this talk, I will share our experiences in developing LIBSVM and LIBLINEAR. LIBSVM
(Chang and Lin, 2011): One of the most popular SVM packages; cited 10,000 times on
Google Scholar LIBLINEAR (Fan et al., 2008): A library for large linear classification;
Structure Of Customer Relationship Management Systems In Data Mining
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ST Sundari, A Padmapriya ,Structure, 2012
ABSTRACT As a young research field, data mining has made broad and significant
progress. Today, data mining is used in a vast array of areas, and numerous commercial
data mining systems are available. On the basis of detailed analysis of the existing CRM
Towards a multiagent-based distributed intrusion detection system using data miningapproaches
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The system that monitors the events occurring in a computer system or a network and
analyzes the events for sign of intrusions is known as Intrusion Detection System (IDS). The
IDS need to be accurate, adaptive, and extensible. Although many established techniques
Survey on mining subjective data on the web
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Abstract In the past years we have witnessed Sentiment Analysis and Opinion Mining
becoming increasingly popular topics in Information Retrieval and Web data analysis. With
the rapid growth of the user-generated content represented in blogs, wikis and Web
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Fundamentals of Data Mining Algorithms
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The traditional research paradigm in the sciences was
hypothesis-driven. Starting with a hypothesis, the researchers designed experiments,
collected data, and after data analysis, either validated the hypothesis or iterated the
A Hybrid Design for Medical Decision Support using Data Mining to Impute Missing Data
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ABSTRACT In this paper, we present a framework which enables medical decision making
in the presence of partial information, leveraging ontological representations and machine
learning techniques to enhance existing patient datasets. We demonstrate its
Electronic Textbooks and Data Mining
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Education is known to be the key determinant of economic growth and prosperity
While the issues in devising a high-quality educational system are multi-faceted and
complex, textbooks are acknowledged to be the educational input most consistently
Analysis on Households Registered/Working through Data Mining Techniques on NREGS (National Rural Employment Guarantee Scheme) Data of Andhra
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MU Rani ,Analysis, 2012
Abstract: Data mining merged the ideas from statistics, machine learning, databases and
parallel computing. The data mining techniques such as characterization, discrimination,
classification, clustering, outlier and trend analysis, etc. are applied on National Rural
Data mining methodology in perspective of manufacturing databases
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M Shahbaz, M Shaheen, M Aslam, S Ahsan ,Life Science , 2012
Abstract: In recent years data mining has become a very popular technique for extracting
information from the database in different areas due to its flexibility of working on any kind of
databases and also due to the surprising results. This paper is an attempt to introduce
The Application Analysis of Clustering and Partitioning Algorithm in Web Data Mining
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GF Kuang, ML Song ,Advances in Computer Science and Information , 2012 ,Springer
Web Data Mining is the primary solution source model of semi-structured data and semi-
structured data model, query and integration issues. The basic idea of clustering is to define
the similarity between the distance, the distance that represents the data between the data
Homogeneous and heterogeneous distributed classification for pocket data mining
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Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad
hoc distributed environments. Advances in mobile devices like smart phones and tablet
computers have made it possible for a wide range of applications to run in such an
Data mining then, now, and next
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D Hand ,2012
The study of genomics increasingly is becoming a field that is dominated by the growth in
the size of data and the responses by the broader scientific community to effectively use and
manage the resulting derived information 40 Gb per day on a singe sequencer, and
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