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methods of data mining in iran

Realdata comparison of data mining methods in prediction of coronary artery disease in Iran Azam Dekamin1 Ahmad Shaibatalhamdi 2 Received 3 Dec 2016 Accepted 27 Jan 2017 ABSTRACT Introduction Cardiovascular diseases are currently of broad prevalence and constitute one of the major causes of mortality in different

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RealData Comparison of Data Mining Methods in Prediction
RealData Comparison of Data Mining Methods in Prediction

RealData Comparison of Data Mining Methods in Prediction of Diabetes in Ir an Lily Tapak MSc 1 Hossein Mahjub PhD 2 Omid Hamidi MSc 3 Jalal Poorolajal PhD 2

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RealData Comparison of Data Mining Methods in
RealData Comparison of Data Mining Methods in

monly used data mining methods namely LR LDA NN FCM RF and SVM and apply these methods to distinguish people with either undiagnosed diabetes or prediabetes from people without these conditions in the Iranian population II Methods 1 Data Source This study used a data set obtained from the Iranian national

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Use of datamining to support realworld cost analyses An
Use of datamining to support realworld cost analyses An

Oct 01 2018 · Use of datamining to support realworld cost analyses An example using HER2positive breast cancer in Iran Ansaripour A1 Zendehdel K2 Tadayon N3 Sadeghi F2 Uylde Groot CA1 Redekop WK1 Author information 1Erasmus School of Health Policy and Management Erasmus University Rotterdam Rotterdam The Netherlands

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Applying Data Mining Methods  JISE
Applying Data Mining Methods JISE

analysis by using data mining approach A case study on one of the Iranian manufactures Rouzbeh Ghousi Department of Industrial Engineering Iran University of Science and Technology Iran ghousi Abstract Uncertain and stochastic states have been always taken into consideration in the fields of accident and risk management and

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Combination of separation methods and data mining
Combination of separation methods and data mining

According to the results the numbers of 1 and 2 are considered as the weights belonging to the background and anomalous values respectively and then prediction process will be run via contribution of 3 data mining methods 43 Forecasting or data mining methods 431 KNN

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Data Mining Methods  Top 8 Types Of Data Mining Method
Data Mining Methods Top 8 Types Of Data Mining Method

There are many methods used for Data Mining but the crucial step is to select the appropriate method from them according to the business or the problem statement These data mining Methods help in predicting the future and then making decisions accordingly

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The 7 Most Important Data Mining Techniques  Data
The 7 Most Important Data Mining Techniques Data

Data mining is highly effective so long as it draws upon one or more of these techniques 1 Tracking patterns One of the most basic techniques in data mining is learning to recognize patterns in your data sets This is usually a recognition of some aberration in your data happening at regular intervals or an ebb and flow of a certain variable over time

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A Comparison Between Data Mining Prediction
A Comparison Between Data Mining Prediction

to create a model and apply data mining algorithms on Ahanpishegan’s data Finally we provide some suggestions to improve the model for further studies 2 Required concepts 21 Data mining Data mining discovers hidden relationships in data in fact it is part of a wider process called “knowledge discovery”

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Data Mining Methods and Models  Wiley Online Books
Data Mining Methods and Models Wiley Online Books

Nov 11 2005 · Data Mining Methods and Models Applies a white box methodology emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets including a detailed case study Modeling Response to DirectMail

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Data mining  Wikipedia
Data mining Wikipedia

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning statistics and database systems Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information with intelligent methods from a data set and transform the information into a comprehensible structure for

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Data Mining Techniques for Anti Money Laundering
Data Mining Techniques for Anti Money Laundering

Therefore unsupervised data mining technique will be more effective to detect new patterns of money laundering and can be crucial to enhance learning models based on classification methods Of course the development of new methods will be very useful to increase the accuracy of performance

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PREDICTING DROPOUT STUDENT AN APPLICATION
PREDICTING DROPOUT STUDENT AN APPLICATION

Predicting Dropout Student An Application of Data Mining Methods in an Online Education Program Erman Yukselturk et al European Journal of Open Distance and e‐Learning – Vol 17 No 1 118

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4 Important Data Mining Techniques  Data Science
4 Important Data Mining Techniques Data Science

Jun 08 2018 · Data Mining is an important analytic process designed to explore data Much like the reallife process of mining diamonds or gold from the earth the most important task in data mining is to extract nontrivial nuggets from large amounts of data

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Data mining techniques – IBM Developer
Data mining techniques – IBM Developer

Data mining itself relies upon building a suitable data model and structure that can be used to process identify and build the information that you need Regardless of the source data form and structure structure and organize the information in a format that allows the data mining to take place in as efficient a model as possible

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Know The Best 7 Difference Between Data Mining Vs Data
Know The Best 7 Difference Between Data Mining Vs Data

The goal of Data Mining is to make data more usable while the Data Analysis helps in proving a hypothesis or taking business decisions Data Mining doesn’t need any preconceived hypothesis to identify the pattern or trend in the data On the other hand Data Analysis tests a given hypothesis

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RootCause and Defect Analysis based on a Fuzzy Data
RootCause and Defect Analysis based on a Fuzzy Data

1 Identification of the process parameters that can be used to control and reduce output product defects 2 Identification of the defects that most probably have common roots 3 Root cause analysis manufacturing processes data mining techniques can be used

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Data Mining Methods and Models  Wiley Online Books
Data Mining Methods and Models Wiley Online Books

Nov 11 2005 · He has also worked as a data mining consultant for Connecticutarea companies He is the author of Discovering Knowledge in Data An Introduction to Data Mining Wiley and is currently working on the third book of his threevolume set on data mining Data Mining the Web Uncovering Patterns in Web Content Structure and Usage with Zdravko Markov PhD scheduled to be

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Comparing data mining classifiers to predict spatial
Comparing data mining classifiers to predict spatial

Soil survey and mapping in Iran follow the traditional approach which requires the collection of a large amount of soil morphological data eg soil colour soil texture and other information TaghizadehMehrjardi et al 2014 The result has been the preparation of a soil map across the country and at a scale of 11000000

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Comprehensive Guide on Data Mining and Data Mining
Comprehensive Guide on Data Mining and Data Mining

Mar 05 2017 · Data mining is often an integral part of those researches and studies STEPS IN DATA MINING Data mining is a process which means that anyone using it should go through a series of iterative steps or phases The number of steps vary with some packing the whole process within 5 steps

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Binning Methods for Data Smoothing
Binning Methods for Data Smoothing

Binning method can be used for smoothing the data Mostly data is full of noise Data smoothing is a data preprocessing technique using a different kind of algorithm to remove the noise from the data set This allows important patterns to stand out Unsorted data for price in dollars Before sorting 8 16 9 15 21 21 24 30 26 27 30 34 First of all sort the data

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Data Mining Techniques
Data Mining Techniques

There are several major data mining techniques have been developing and using in data mining projects recently including association classification clustering prediction sequential patterns and decision tree We will briefly examine those data mining techniques in the following sections

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An Introduction to Data Mining for Marketing and Business
An Introduction to Data Mining for Marketing and Business

Mar 31 2011 · Types of Data Mining Methods and Marketing Data mining methods can be divided in multiple ways However most books on the topic and especially those related to marketing and business intelligence will generally divide data mining methods into two types the ones related to supervised learning and the ones related to unsupervised learning

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What is prediction in data mining  Quora
What is prediction in data mining Quora

Prediction is nothing but finding out the knowledge or some pattern from the large amounts of data For example In credit card fraud detection history of data for a particular person’s credit card usage has to be analysed If any abnormal patte

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Case Study Data Mining of Associate Degree Accepted
Case Study Data Mining of Associate Degree Accepted

2 Data Sets Data set used in this study is a sample of accepted candi dates in modular associate degree in 2008 and 2009 which contains about 20000 of accepted candidates during two years Features of this data set are described in Table 1 Data Preparing Data preparing is

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Data Mining in Medical Billing and Coding  Healthcare
Data Mining in Medical Billing and Coding Healthcare

Implementation of Data Mining for Medical Billing and coding The healthcare industry is experiencing a revolution one the likes of which have never been witnessed in the past The crux of this revolution involves the adoption of data mining strategies to systematize the medical coding and billing industry within this panorama

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Data Mining Visualization Techniques
Data Mining Visualization Techniques

Lesson Summary To recap data mining is the process of organizing and recognizing information in order to predict new information This process makes use of techniques from databases statistics computer science artificial intelligence and machine learning

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