data mining IEEE PAPER , IEEE PROJECT
Data mining is a process used by companies to turn raw data into useful information by using software Data Mining is an analytic process designed to explore data (usually large amounts of data typically business or market related also known as “big data”) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns
Data Mining Tool for Academic Data Exploitation
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The ultimate goal of SPEET project is the development of an WEB-based tool to disseminate the main intellectual output in form of user-friendly and easily accessible software tool. The WEB-tool is accessible from speet. uab. cat is intended to make accessible by other facultiesNatural hazards such as landslides, floods, earthquakes, forest fires, droughts and erosion processes impact severely every year structures, infrastructures and population producing financial damages and human casualties. Based on the Centre for Research on the
Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining
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We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data
Social Big Data : Mining , Applications, and Beyond
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The social nature of Web 2.0 leads to the unprecedented growth of discussion forums, product review sites, microblogging, and other social media platforms. Existing social media data mining research can be broadly divided into two groups. The content-based approach focuses on extracting
Prediction of Selected Reproductive Traits of Indigenous Harnai Sheep under the Farm Management System via various Data Mining Algorithms
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In this study, an attempt was made at predicting the values of selected reproductive parameters in Harnai sheep using different data mining algorithms (artificial neural networks- ANN, classification and regression trees-CART, chi-square automatic interaction detector
Co-clustering Numerical Data under User-defined Constraints STATISTICAL ANALYSIS AND DATA MINING (2010) 3
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In the generic setting of objects attributes matrix data analysis, co-clustering appears as an interesting unsupervised data mining method. A co-clustering task provides a bi-partition made of coclusters: each co-cluster is a group of objects associated to a group of attributes
Principles of Green Data Mining
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This paper develops a set of principles for green data mining , related to the key stages of business un-derstanding, data understanding, data preparation, modeling, evaluation, and deployment. The principles are grounded in a review of the Cross Industry Stand-ard
Data mining for energy analysis of a large data
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Data mining for energy analysis of a large data set of flats
Predicting University Dropout through Data Mining : A Systematic Literature
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Objectives: To make a systematic review of literature on the prediction of university student dropout through data mining techniques. Methods/Analysis: The study was developed as a systematic review of the literature of empirical research results regarding the prediction of
A proposed Model for Predicting Employees Performance Using Data Mining Techniques: Egyptian Case Study
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Human Resources Management (HRM) has become one of the essential interests of managers and decision makers in almost all types of businesses to adopt plans for correctly discovering highly qualified employees. Accordingly, managements become interested
Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques
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Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this
Gender Differences in Science and Engineering: A Data Mining Approach
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In this paper, we describe a data -intensive approach to study gender differences in Science, Technology, Engineering and Mathematics (STEM). We apply deep learning, text mining and statistical methods to unique academic datasets, including undergraduate admission
Transparently mining data from a medium-voltage distribution network: a prognostic-diagnostic analysis
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With the shift from the traditional electric grid to the smart grid paradigm, huge amounts of data are collected during system operations. Data analytics become of fundamental importance in power networks to enable predictive maintenance, to perform effectiveDynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these subproblems beginning from the simplest ones. The aim of a dynamic programming algorithm is to find an
Business Intelligence Improved by Data Mining Algorithms and Big Data Systems: An Overview of Different Tools Applied in Industrial Research
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The proposed paper shows different tools adopted in an industry project oriented on business intelligence (BI) improvement. The research outputs concern mainly data mining algorithms able to predict sales, logistic algorithms useful for the management of the
Introduction to the Minitrack on Data , Text, and Web Mining for Business Analytics
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This mini-track has a total of nine papers that are about developing analytics systems for decision support by means of data , text, or web mining . Five of the nine papers focus on a variety of interesting text mining , natural language processing, and sentiment analysis. The
Paid Advertisement on Facebook: An Evaluation Using a Data Mining Approach
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This paper focuses on evaluating the performance of paid publications (paid ads) on Facebook and proposes a managerial implication to maximize the paid publications performance in reaching as many people as possible with the greatest possibleData mining can discover valuable information from large amounts of data so as to utilize this information to enhance personal or organizational competitiveness. Apriori is a classic algorithm for mining frequent itemsets. Recently, with rapid growth of the Internet as well as
Analysis of Educational Data Mining Techniques
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The prediction analysis is the approach which can predict future possibilities based on the current information. The prediction analysis can be done using the technique of classification and neural networks. Every educational institute aims at delivering quality
Relationship between Regional Economic Development and Ecological Environment Based on Spatial Data Mining
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In order to understand the relationship between economy and environment under the point- axis development model, spatial data mining methods and spatial analysis software were used to analyze the spatial distribution of GDP in the Heilongjiang Province industrial
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