data mining research papers 2012 section 2

 Visual Data Mining in Indian Election System
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TM Kodinariya, MR Seta ,International Journal, 2012
Abstract-A good leaders or Government is the basic need to develop country. In India, who is
largest democratic country in the world people are not fully involved in the selection process
of Leaders. On an average there are 60-65% voting is done. For this purpose we have 

 Transactions on Machine Learning and Data Mining Vol. 5, No. 1 (2012) 1-2© 2012, ibai-publishing
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A Krzyzak ,2012 ,
Abstract. Special issue of Transactions on Machine Learning and Data Mining includes
three papers spanning a wide spectrum of data mining and machine learning fields. The first
paper applies contrasting correlations between databases to detect changes of interest. 

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AY Melnikov, KV Kravchenko ,Modern scientific research , 2012 ,
Abstract The paper describes the work in progress for developing a requirement
engineering method that will allow for a software that precisely meets user’s needs. The
technology integrates tools from different methods and disciplines with recent 

 A Concise Artificial Neural Network in Data Mining
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A Ali ,International Journal of Research in Engineering, 2012 ,
ABSTRACT Data mining a multidisciplinary field, is an analytic process designed to explore
data (typically business or market related) in search of consistent patterns and/or systematic
relationships between variables, and then to validate the findings by applying the detected 

Algorithm to determine the outcome of patients with acute liver failure: a data-mining analysis using decision trees
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N Nakayama, M Oketani, Y Kawamura, M Inao ,Journal of , 2012 ,Springer
Abstract Background We established algorithms to predict the prognosis of acute liver failure
(ALF) patients through a data-mining analysis, in order to improve the indication criteria for
liver transplantation. Methods The subjects were 1,022 ALF patients seen between 1998 

Visual Data Mining and Discovery with Binarized Vectors
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B Kovalerchuk, F Delizy, L Riggs, E Vityaev  Data Mining: Foundations , 2012 ,Springer
The emerging field of Visual Analytics combines several fields where Data Mining and
Visualization play leading roles. The fundamental departure of visual analytics from other
approaches is in extensive use of visual analytical tools to discover patterns not only to 

Construction of Web-Based, Service-Oriented Information Networks: A Data Mining Perspective
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J Han ,Web-Age Information Management, 2012 ,Springer
Mining directly on the existing networks formed by explicit webpage links on the World-Wide
Web may not be so fruitful due to the diversity and semantic heterogeneity of such web-links.
However, construction of service-oriented, semi-structured information networks from the 

 Insurance Dynamics–A Data Mining Approach for Customer Retention in Health Care Insurance Industry
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Abstract: Extraction of customer behavioral patterns is a complex task and widely studied for
various industrial applications under different heading viz., customer retention management,
business intelligence and data mining. In this paper, authors experimented to extract the 

Mining fuzzy association rules from low-quality data
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Mining fuzzy association rules from low-quality data  Abstract Data mining is most commonly used
in attempts to induce association rules from databases which can help decision-makers easily
analyze the data and make good decisions regarding the domains concerned. 

 Data Mining Application in Enrollment Management: A Case Study
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SK Yadav, S Pal, SK Soni ,International , 2012
ABSTRACT In the last two decades, number of Higher Education Institutions (HEI) grows
rapidly in India. This causes a cut throat competition among these institutions while attracting
the student to get admission in these institutions. Most of the institutions are opened in self 

 K-Means Clustering in Spatial Data Mining using Weka Interface
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R Sharma, MA Alam, A Rani ,IJCA Proceedings on , 2012
ABSTRACT Clustering techniques have a wide use and importance nowadays and this
importance tends to increase as the amount of data grows. K-means is a simple technique
for clustering analysis. Its aim is to find the best division of n entities into k groups (called 

AITION: a scalable platform for interactive data mining
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H Dimitropoulos, H Kllapi, O Metaxas ,Scientific and Statistical , 2012 ,Springer
AITION is a scalable, user-friendly, and interactive data mining (DM) platform, designed for
analyzing large heterogeneous datasets. Implementing state-of-the-art machine learning
algorithms, it successfully utilizes generative Probabilistic Graphical Models (PGMs) 

 Framework to Study Impact of Environmental Parameters on Human Health Using GIS andData Mining
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S Vitkar, R Singhal ,2012 ,
Abstract—Health is of major concern among people. There are various factors which could
affect human health. In this paper we propose a framework to analyze various environmental
parameters like air and water pollutants and to understand their impact on human health. 

Linguistic fuzzy rules in data mining: follow-up Mamdani fuzzy modeling principle
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From the definition of fuzzy sets by Zadeh in 1965, fuzzy logic has become a significant area
of interest for researchers on artificial intelligence. In particular, Professor Mamdani was the
pioneer who investigated the use of fuzzy logic for interpreting the human derived control 

 Formal Frame for Data Mining with Association Rules–a Tool for Workflow Planning
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J Rauch, M Šimunek ,5 th PLANNING TO LEARN WORKSHOP WS28 AT , 2012 ,
The goal of this extended abstract is to contribute to the forum for research on construction of
data mining workflows. We briefly introduce a formal framework called FOFRADAR (FOrmal
FRAmework for Data mining with Association Rules) and then we outline how it can be 

 A Mid–Point based k-mean Clustering Algorithm for Data mining
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N Aggarwal, K Aggarwal ,International Journal, 2012
Abstract—In k-means clustering algorithm, the number of centroids is equal to the number of
the clusters in which data has to be partitioned which in turn is taken as an input parameter.
The initial centroids in original k-means are chosen randomly from the given dataset and 

 Spatial Data Mining using Cluster Analysis
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CNS Kumar, VS Ramulu, KS Reddy, S Kotha ,2012
Abstract Data mining, which is refers to as Knowledge Discovery in Databases (KDD),
means a process of nontrivial exaction of implicit, previously useful and unknown
information such as knowledge rules, descriptions, regularities, and major trends from 

A data mining framework for product and service migration analysis
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ST Au, R Duan, W Jiang ,Annals of Operations Research, 2012 ,Springer
Abstract With new technologies or products invented, customers migrate from a legacy
product to a new product from time to time. This paper discusses a time series data mining
framework for product and service migration analysis. In order to identify who migrate, how 

 Prediction of Length of ICU Stay Using Data-mining Techniques: an Example of Old Critically Ill Postoperative Gastric Cancer Patients
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XC Zhang, ZD Zhang, DS Huang ,Asian Pacific Journal of Cancer , 2012 ,
Abstract Objective: With the background of aging population in China and advances in
clinical medicine, the amount of operations on old patients increases correspondingly, which
imposes increasing challenges to critical care medicine and geriatrics. The study was 

 Discovery of students’ academic patterns using data mining techniques
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MS Acharya ,International Journal, 2012
Abstract-Knowledge discovery is an emerging field which combines the techniques from
mathematics, statistics, algorithms and Artificial Intelligence to extract the knowledge. Data
mining is a main phase of Knowledge Discovery in Databases (KDD) for extracting the 

 Analyzing manuscript traditions using constraint-based data mining
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Abstract. Data mining tasks and algorithms are often categorized as belonging to one of a
few specific types: clustering, association rule discovery, probabilistic modeling, etc. For
some time now, it has been recognized that concrete tasks do not always fit nicely in this 

Efficient model selection for large-scale nearest-neighbor data mining
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One of the most widely used models for large-scale data mining is the k-nearest neighbor (k-
nn) algorithm. It can be used for classification, regression, density estimation, and
information retrieval. To use k-nn, a practitioner must first choose k, usually selecting the k 

 Privacy Preserving for Feature Selection in Data Mining Using Centralized Network
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HK Bhuyan, M Mohanty, SR Das ,2012
Abstract This paper proposed a feature selection with privacy preservation in centralized
network. Data can be preserved for privacy by perturbation technique as alias name. In
centralized data evaluation, it makes data classification and feature selection for data