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.

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.

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.

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.

scholarly journals Career and Skills Recommendations using Data Mining Technique: Matching Right People for Right Profession, in Pakistani Context

2018 ◽  
pp. 93-101
Author(s):  
◽  
◽  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Educational Background ◽  
The Internet ◽  
Job Seekers ◽  
Data Mining Technique ◽  
Association Rules Mining ◽  
Mining Technique ◽  
Using Data ◽  
Different Sources

There are a number of recommendation systems available on the internet for the help of jobseekers. These systems only generate job recommendations for people on the basis of input entered by user. The problem observed in Pakistani people is they are not clear in which field they should start or switch working. Before searching and applying for a job, one should be clear about his/her profession and important skills regarding selected profession. Based on above issues, there is a need to design such a system that can overcome the problem of profession selection and skills suggestions so that it can be easy for a jobseeker to apply for a specific job. In this research, the problem which is discussed above is resolved by proposing a model by using Association Rules Mining, a data mining technique. In this model, professions are recommended to job seekers by matching the profile of applicant or job seeker with those persons who have same profile like educational background, professional skills and the type of jobs which they are doing. The data collected for this research itself is a major contribution as we collected it from different sources. We will make this data publically available for others so that they can use for further research.

scholarly journals Generic Disease Prediction using Symptoms with Supervised Machine Learning

2019 ◽  
pp. 1082-1086 ◽  
Author(s):  
Ashish Kailash Pal ◽  
Pritam Rawal ◽  
Rahil Ruwala ◽  
Vaibhavi Patel
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Disease Prediction ◽  
Data Mining Technique ◽  
Mining Technique ◽  
The Common ◽  
Using Data ◽  
Health Organization

Data Mining and Machine Learning plays most inspiring area of research that become most popular in health organization. It also plays a vital part to uncover new patterns in medicinal science and services association which thusly accommodating for all the parties associated with this field. This project intend to form a diagnostic model of the common diseases based on the symptoms by using data mining technique such as classification in health domain. In this project, we are going to use algorithms like Random forest, Naive Bayes which can be utilized for health care diagnosis. Performances of the classifiers are compared to each other to find out highest accuracy. This also helps us to find out persons who are affected by the infection. The test based on the outcomes of the diseases.

scholarly journals Classification of Hematological Data Using Data Mining Technique to Predict Diseases

2018 ◽  
Vol 06 (04) ◽  
pp. 76-83 ◽  
Author(s):  
Fahmida Akter ◽  
Md Altab Hossin ◽  
Golam Moktader Daiyan ◽  
Md. Motaher Hossain
Keyword(s):  
Data Mining ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Using Data

scholarly journals Classification of Pruning Methodologies for Model Development using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 2043-2047 ◽  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Model Development ◽  
Data Mining Techniques ◽  
Rule Set ◽  
Using Data ◽  
Rule Pruning ◽  
Class Association Rule

Knowledge discovery process deals with two essential data mining techniques, association and classification. Classification produces a set of large number of associative classification rules for a given observation. Pruning removes unnecessary class association rules without losing classification accuracy. These processes are very significant but at the same time very challenging. The experimental results and limitations of existing class association rules mining techniques have shown that there is a requirement to consider more pruning parameters so that the size of classifier can be further optimized. Here through this paper we are presenting a survey various strategies for class association rule pruning and study their effects that enables us to extract efficient compact and high confidence class association rule set and we have also proposed a pruning methodology..

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