A Survey on Building Recommendation Systems Using Data Mining Techniques

2022 ◽  
pp. 24-56
Author(s):  
Rajab Ssemwogerere ◽  
Wamwoyo Faruk ◽  
Nambobi Mutwalibi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Recommender Systems ◽  
Performance Measures ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Learning Hypothesis ◽  
Depth Study ◽  
And Performance ◽  
Using Data

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

scholarly journals Impact of Data Mining Technique in Education Institutions

2015 ◽  
Vol 4 (02) ◽  
pp. 01-07
Author(s):  
Mr. Bhushan Bandre, Ms. Rashmi Khalatkar
Keyword(s):  
Data Mining ◽  
Educational Institution ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Admission Process ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Major Parameter ◽  
Using Data ◽  
Major Decision

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.

Homeland Security Data Mining and Link Analysis

2011 ◽  
pp. 566-569
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Homeland Security ◽  
Link Analysis ◽  
Machine Learning Techniques ◽  
Data Mining Technique ◽  
Learning Techniques ◽  
Terrorist Groups ◽  
Using Data ◽  
Terrorist Events

Data mining is the process of posing queries to large quantities of data and extracting information often previously unknown using mathematical, statistical, and machine-learning techniques. Data mining has many applications in a number of areas, including marketing and sales, medicine, law, manufacturing, and, more recently, homeland security. Using data mining, one can uncover hidden dependencies between terrorist groups as well as possibly predict terrorist events based on past experience. One particular data-mining technique that is being investigated a great deal for homeland security is link analysis, where links are drawn between various nodes, possibly detecting some hidden links.

scholarly journals Prediction of Blood Donors Population Using Data Mining Classification Technique

2021 ◽  
pp. 25-27
Author(s):  
Aman Paul ◽  
Daljeet Singh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Association Rules ◽  
User Interfaces ◽  
Relational Data ◽  
Easy Access ◽  
Data Mining Technique ◽  
Open Source Data ◽  
Using Data

Data mining is a technique that finds relationships and trends in large datasets to promote decision support. Classification is a data mining technique that maps data into predefined classes often referred as supervised learning because classes are determined before examining data. Different classification algorithms have been proposed for the effective classification of data. Among others, Weka is an open-source data mining software with which classification can be achieved. It is also well suited for developing new machine learning schemes. It allows users to quickly compare different machine learning methods on new datasets. It has several graphical user interfaces that enable easy access to the underlying functionality. CBA is a data mining tool which not only produces an accurate classifier for prediction, but it is also able to mine various forms of association rules. It has better classification accuracy and faster mining speed. It can build accurate classifiers from relational data and mine association rules from relational data and transactional data. CBA also has many other features like cross validation for evaluating classifiers and allows the user to view and to query the discovered rules.

Homeland Security Data Mining and Link Analysis

2008 ◽  
pp. 3639-3644
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Homeland Security ◽  
Link Analysis ◽  
Machine Learning Techniques ◽  
Data Mining Technique ◽  
Learning Techniques ◽  
Terrorist Groups ◽  
Using Data ◽  
Terrorist Events

Data mining is the process of posing queries to large quantities of data and extracting information often previously unknown using mathematical, statistical, and machine-learning techniques. Data mining has many applications in a number of areas, including marketing and sales, medicine, law, manufacturing, and, more recently, homeland security. Using data mining, one can uncover hidden dependencies between terrorist groups as well as possibly predict terrorist events based on past experience. One particular data-mining technique that is being investigated a great deal for homeland security is link analysis, where links are drawn between various nodes, possibly detecting some hidden links.

Classification of Failures in Photovoltaic Systems using Data Mining Techniques

Big Data ◽  
2016 ◽  
pp. 1347-1366
Author(s):  
Lucía Serrano-Luján ◽  
Jose Manuel Cadenas ◽  
Antonio Urbina
Keyword(s):  
Data Mining ◽  
Environmental Parameters ◽  
Weather Conditions ◽  
Environmental Data ◽  
Photovoltaic System ◽  
Data Mining Techniques ◽  
Learning Techniques ◽  
And Performance ◽  
Using Data ◽  
Bad Weather

Data mining techniques have been used on data collected from a photovoltaic system to predict its generation and performance. Nevertheless, up to date, this computing approach has needed the simultaneous measurement of environmental parameters that are collected by an array of sensors. This chapter presents the application of several computing learning techniques to electrical data in order to detect and classify the occurrence of failures (i.e. shadows, bad weather conditions, etc.) without using environmental data. The results of a 222kWp (CdTe) case study show how the application of computing learning algorithms can be used to improve the management and performance of photovoltaic generators without relying on environmental parameters.

Classification of Failures in Photovoltaic Systems using Data Mining Techniques

2015 ◽  
pp. 300-319 ◽  
Author(s):  
Lucía Serrano-Luján ◽  
Jose Manuel Cadenas ◽  
Antonio Urbina
Keyword(s):  
Data Mining ◽  
Environmental Parameters ◽  
Weather Conditions ◽  
Environmental Data ◽  
Photovoltaic System ◽  
Data Mining Techniques ◽  
Learning Techniques ◽  
And Performance ◽  
Using Data ◽  
Bad Weather

Data mining techniques have been used on data collected from a photovoltaic system to predict its generation and performance. Nevertheless, up to date, this computing approach has needed the simultaneous measurement of environmental parameters that are collected by an array of sensors. This chapter presents the application of several computing learning techniques to electrical data in order to detect and classify the occurrence of failures (i.e. shadows, bad weather conditions, etc.) without using environmental data. The results of a 222kWp (CdTe) case study show how the application of computing learning algorithms can be used to improve the management and performance of photovoltaic generators without relying on environmental parameters.

scholarly journals Improving electronic customers' profile in recommender systems using data mining techniques

2011 ◽  
Vol 1 (4) ◽  
pp. 449-456 ◽  
Author(s):  
Mohammad Julashokri ◽  
Mohammad Fathian ◽  
Mohammad Reza Gholamian ◽  
Ahmad Mehrbod
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals AN EVALUATION OF SWINE FLU (INFLUENZA A H3N2V) VIRUS PREDICTION USING DATA MINING AND CONVENTIONAL NEURAL NETWORK TECHNIQUES

2021 ◽  
Vol 12 (4) ◽  
pp. 1377-1386
Author(s):  
Pilla Srinivas, Et. al.
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Influenza A ◽  
Medical Patient ◽  
Swine Flu ◽  
Research Trend ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Healthcare Data ◽  
Using Data

Nowadays, The health care commercial enterprise collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. Data mining plays a significant role in predicting diseases. The database report of medical patient is not more efficient, currently we made an Endeavour to detect the most widely spread disease in all over the world named Swine flu. Swine flu is a respiratory disease which has Numeral number of tests must be requisite from the patient for detecting a disease. Advanced data mining techniques gives us help to remedy this situation. In this work we describes about a prototype using data mining techniques, namely Naive Bayes Classifier. The Data mining is an emerging research trend which helps in finding accurate solutions in many fields. This paper highlights the various data mining technique and Convolution Neural Network used for predicting swine flu diseases.

scholarly journals Potable Water Order Forecasting System Using Data Mining Technique

2021 ◽  
Vol 11 (6) ◽  
pp. 1-8
Author(s):  
Phatarapon Vorapracha ◽  
Keyword(s):  
Data Mining ◽  
Drinking Water ◽  
Potable Water ◽  
Bayesian Classification ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
C4.5 Algorithm ◽  
Forecasting System ◽  
Using Data

Potable water order forecasting system using data mining technique. It aims to analyze, design and develop potable water order forecasting system using data mining technique. There is a comparison data mining techniques were compared using the C4.5 algorithm and Bayesian classification algorithm. The researcher found that the C4.5 algorithm is more suitable for drinking water ordering system. This web application system allows the system to predict each customer's drinking water orders. Subscription support ordering, drinking water and bank payment. In terms of user interaction and use the MySQL database program to organize the system database. The result of development potable water order forecasting system using data mining technique. Have tested data mining techniques were compared using the C4.5 algorithm and Bayesian classification algorithm. The researcher found that the C4.5 algorithm is more suitable for drinking water ordering system. From data research results using data in 9 months of training and 2 months of testing, it was found that the accuracy was 85.59%. C4.5 algorithm and test the system from the evaluation of 2 administrators, 3 employees and 5 customers, total 10 people with average mean of 4.20 .

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