A Data Mining System for Potential Customers Based on One-Class Support Vector Machine

Crime rate is expanding extremely more because of destitution and joblessness. With the current crime investigation techniques, officers need to invest a great deal of energy just as labor to recognize suspects and criminals. Anyway crime investigation procedure should be quicker and dynamic. As huge amount of data is gathered during crime investigation, data mining is a methodology which can be valuable in this viewpoint. Data mining is a procedure that concentrates valuable data from enormous amount of crime data with the goal that potential suspects of the crime can be recognized productively. Quantities of data mining techniques are accessible. Utilization of specific data mining system has more prominent impact on the outcomes acquired. So the exhibition of three data mining techniques will be analyzed against test crime and criminal database and best performing algorithm will be utilized against test crime and criminal database to recognize potential suspects of the crime. Data mining is a procedure of separating information from colossal amount of data put away in databases, data stockrooms and data archives. Clustering is the way toward consolidating data objects into gatherings. Here taken the Crime dataset from Chicago police website and implemented in MATLAB utilizing Support Vector Machine algorithm.

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The economics of selection of mail orders Drs. Zahavi and Levin are the masterminds behind the development of AMOS, a customized predictive modeling system for the Franklin Mint in Philadelphia, and GainSmarts, a general purpose data mining system that is the two-time winner of the KDD-CUP competition for the best data mining tools (1997 and 1998) sponsored by the American Association for Artificial Intelligence.

Journal of Interactive Marketing ◽

10.1002/dir.1016.abs ◽

2001 ◽

Vol 15 (3) ◽

pp. 53

Author(s):

Nissan Levin ◽

Jacob Zahavi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Predictive Modeling ◽

American Association ◽

General Purpose ◽

Mining System ◽

Data Mining System ◽

Mining Tools ◽

Selection Of

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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Data Mining System and Construction Analysis of Skilled Talents based on Big Data

Journal of Physics Conference Series ◽

10.1088/1742-6596/1744/3/032021 ◽

2021 ◽

Vol 1744 (3) ◽

pp. 032021

Author(s):

Yibo Guan

Keyword(s):

Data Mining ◽

Big Data ◽

Mining System ◽

Data Mining System

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An intelligent data mining system for drop test analysis of electronic products

IEEE Transactions on Electronics Packaging Manufacturing ◽

10.1109/6104.956808 ◽

2001 ◽

Vol 24 (3) ◽

pp. 222-231 ◽

Cited By ~ 16

Author(s):

Chi Zhou ◽

P.C. Nelson ◽

Weimin Xiao ◽

T.M. Tirpak ◽

S.A. Lane

Keyword(s):

Data Mining ◽

Drop Test ◽

Mining System ◽

Test Analysis ◽

Electronic Products ◽

Data Mining System

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Criminal Network Analysis inside Law Enforcement Agencies: A Data-Mining System Approach under the National Intelligence Model

International Journal of Police Science & Management ◽

10.1350/ijps.2013.15.4.321 ◽

2013 ◽

Vol 15 (4) ◽

pp. 323-337 ◽

Cited By ~ 7

Author(s):

Patrick Seidler ◽

Rick Adderley

Keyword(s):

Data Mining ◽

Law Enforcement ◽

Network Analysis ◽

System Approach ◽

Law Enforcement Agencies ◽

Mining System ◽

Criminal Network ◽

Data Mining System ◽

Criminal Network Analysis

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Data Mining Approach to Analyze COVID-19 Clinical Dataset

10.53350/pjmhs211561812 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1812-1819

Author(s):

Azita Yazdani ◽

Ramin Ravangard ◽

Roxana Sharifian

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Clinical Signs ◽

Study Data ◽

Mining Machine ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

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Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.295-298.644 ◽

2013 ◽

Vol 295-298 ◽

pp. 644-647 ◽

Cited By ~ 1

Author(s):

Yu Kai Yao ◽

Hong Mei Cui ◽

Ming Wei Len ◽

Xiao Yun Chen

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Classification Accuracy ◽

Prediction Accuracy ◽

Support Vector ◽

Disease Prediction ◽

Data Mining Algorithm ◽

Grid Search ◽

Mining Algorithm ◽

Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

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