Search
129 articles for “Data mining Technique”
-
Comparative Study of Bayes Net and Multilayer Perceptron Model using ECG Signals
Abstract: In recent days, heart abnormalities are common among people regardless of their age. Heart abnormalities must be detected and treated in very initial stage which protects people from major heart abnormalities. Sometimes, physician feels difficulty in classifying heart abnormalities from extracted features. This problem can be solved with an excellent data mining technique called classification. Classification is a data mining technique which is used to label tuples. In this paper …
Published in Recent Trends in Parallel Computing · Vol. 2, Issue 2, 2015 · pp. 14–17 Read article
-
Scientific Paper Reviews Identification by Sentiment Analysis in Data Mining
Abstract: AbstractSentiment analysis (SA) is one of the rapidly growing fields of research in computer science and makes it difficult to track all activities in the area. Analyzes of thoughts and/or perceptions are used for automating subjective information identification such as opinions, attitudes, emotions, and feelings. Hundreds of thousands are concerned about science and take a long time to select appropriate papers for their research. These opinions are numerous and SA …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 7, Issue 3, 2020 · pp. 1–9 Read article
-
A Study on Social Network Mining
Abstract: AbstractA rapid increase has been seen in the recent decade in the usage of social media. Web and web 2.0 has made it easy for users to reach out to Twitter, Facebook and other social media platforms. Most of the users depend on these social media platforms for news and opinions on trending topics. This type of dependence on these social media platforms have produced large amount of data in …
Published in Journal of Advanced Database Management & Systems · Vol. 6, Issue 2, 2019 · pp. 36–39 Read article
-
A Survey of Fraud Detection Techniques for Credit Card Based Transaction Processing
Abstract: The wide emergence of electronic-commerce has widened the extensive usage of credit card for online transactions. However, there is also a high rise in malicious transaction and fraudulent associated with the credit cards. In this study, we present several models and algorithm used in data mining for the detection of such malicious fraudulent or thefts. Such algorithm learns the transaction patterns and clusters the pattern of sequences usually involving with …
Published in Recent Trends in Parallel Computing · Vol. 2, Issue 1, 2015 · pp. 10–15 Read article
-
Unleashing the Power of Mathematics in Natural Language Processing: Sentiment Analysis Perspectives
Abstract: Sentiment analysis (SA), which utilizes natural language processing (NLP), computational linguistics, text analysis, image processing, and video processing to extract and analyze subjective information from the internet, social media, and other sources, is becoming increasingly popular in both the business world and the scientific community. It is even possible to model it so that it focuses on polarity, sentiments and emotions, urgency, and even goals. It is able to distinguish …
Published in Recent Trends in Programming languages · Vol. 10, Issue 2, 2023 · pp. 1–9 Read article
-
A Survey on Different Clustering Algorithms with Their Major Features
Abstract: Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. Clustering is used to organize data for efficient retrieval. The aim is to create homogeneous subgroups of examples. The individuals in the same subgroup are similar; the individuals in different subgroups are as different as possible. One of the problems in clustering is the identification of clusters in given data. A popular technique …
Published in Journal of Web Engineering & Technology · Vol. 1, Issue 3, 2014 · pp. 19–24 Read article
-
Data Stream Mining
Abstract: A data stream can be considered as an ordered sequence of data items, where the elements of the series continuously arrive as time progresses. Data stream mining is the procedure of extracting knowledge structures from such continuous, rapid data records. Mining data streams nurtures new problems for the data mining community regarding how to mine continuous high-speed data items that you can only have one look at. Due to this …
Published in Recent Trends in Programming languages · Vol. 5, Issue 1, 2018 · pp. 1–5 Read article
-
Implementation of Data Mining Approach to Find the Adaptability of Students in Online Education during Covid-19 Pandemic
Abstract: The global Covid-19 pandemic has severely affected very aspects of human life, including education. The virus' stunning spread created havoc in the educational system, causing educational institutions to close. As an effect, students must quickly adopt to the change to synchronous online learning. This study identified the different aspects affecting the adaptability level of students in online class. It also identifies the student’s adaptability level in different circumstances in online …
Published in Journal of Artificial Intelligence Research & Advances Read article
-
Disease Prediction System Using Data Mining
Abstract: The project's main goal is to develop a system that allows users to get personalized advice on their health problems using data mining techniques. In today's hectic world, most individuals overlook this asset, which could be due to a lack of time or the intricacy of the massive data available on the internet. Our goal is to evaluate data processing techniques in clinical and health care settings in order to …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 9, Issue 1, 2022 · pp. 9–13 Read article
-
Representative Feature Selection for Efficient Clustering
Abstract: AbstractClustering is one of the widely used data mining techniques. In machine learning, it is an unsupervised learning method that needs training data to be used. The problem with clustering is that it is computationally expensive and takes more time for grouping high-dimensional data. Therefore it is necessary to reduce number of features in the high-dimensional data in order to make the search space reduced. Many techniques came into existance …
Published in Journal Of Network security · Vol. 5, Issue 2, 2017 · pp. 19–27 Read article
-
Big Data in Chemistry: Problems and Answers
Abstract: The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 9–14 Read article
-
Data Mining for E-Commerce and Social Media: Insights and Future Research Directions
Abstract: The fast expansion of e-commerce and social media has heralded a new era of data-rich settings, with enormous quantities of user interactions, preferences, and transactions generated on a daily basis. Data mining has developed as a critical strategy for leveraging big datasets, allowing businesses to gain concrete knowledge and drive decision-making. Data mining in e-commerce improves operational efficiency and user pleasure by allowing for personalized recommendations, consumer segmentation, fraud detection, …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 1, 2025 · pp. 14–23 Read article
-
A Review on Heart Disease Monitoring System by Using Machine Learning
Abstract: Heart disease is one of the most popular diseases which can lead to reduce the lifespan of human beings now a day. Every year 17.5 million people are get dying due to heart disease. Researchers have been using various types of data mining techniques and machine learning algorithms to help in the diagnosis of heart disease and various other type of diseases. Health care Centre collects large amount of data. …
Published in Recent Trends in Programming languages · Vol. 7, Issue 1, 2020 · pp. 1–6 Read article
-
Efficient Identification of Complex Diseases Through Epistasis Computational Models: A Review
Abstract: AbstractGenome-Wide Association Studies (GWAS) identify and characterize the genes that are associated with human diseases. One of the significant ongoing researches in GWAS is to identify the disease susceptible genes through Epistasis. The gene that masks the effects of other genes is called Epistasis. The gene interacts with another gene is known as epistatic or genetic interactions (GGIs). GWAS identifies the genetic variants of Single Nucleotide Polymorphism and also the …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 7, Issue 2, 2020 · pp. 21–32 Read article
-
Comparative Study of Data Mining Tools
Abstract: Data mining is a process which finds useful patterns from large amount of data for an appropriate application. It is a well-built technology with great potential to help companies whose focus is on important information in huge databases. It uses machine learning, statistical and visualization technique to discover and predict knowledge in a form which is easily understandable to the user. Classification is important technique of data mining which employs …
Published in Journal of Advanced Database Management & Systems · Vol. 2, Issue 2, 2015 · pp. 35–41 Read article
-
Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities
Abstract: The exponential growth of digital healthcare data has spurred innovative applications of data mining techniques in medical research and practice. Among these, association rule mining stands out for its ability to uncover meaningful correlations within diverse datasets, such as electronic health records, imaging data, and genetic information. This paper reviews the application of association rule mining in predicting heart diseases, emphasizing its potential to enhance early detection, risk stratification, and …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 29–34 Read article
-
Phishing URL Detection Using Machine Learning Classification Algorithms
Abstract: Phishing attack is utilized to get the data like username, secret phrase, financial balance subtleties, and credit card details. Today, is the most well-known cybercrime. Phishing assaults additionally influence the web-based installment area monetary organization, document facilitating or distributed storage, and numerous others. Phishing assault generally focuses to these Web locales which are connected with the internet-based payment area and Web mail. To stop phishing attacks, a variety of methods …
Published in Journal of Web Engineering & Technology · Vol. 9, Issue 3, 2022 · pp. 22–33 Read article
-
Data Extraction Using NLP for Unstructured Text Categorization
Abstract: Abstract In this research project, I tried to crawl the web and files to create a dataset so that it can be fetched to FRL (Fuzzy rough set-based semi-supervised learning algorithm). The approach used in the project is with the help of semi-supervised learning that made use of unlabeled data for training typically a small amount of labeled data with a large amount of unlabeled data. We de ne and …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 4, Issue 3, 2017 · pp. 30–39 Read article
-
Collocation Pattern Analysis
Abstract: Spatial data mining becomes more attractive and significant as more spatial data is built up in spatial databases. Many GIS applications are using spatial patterns that are equal to association rules of a business data mining, i.e., online transaction processing (OLTP). Mining the spatial collocation patterns is a significant spatial data mining job with broad applications. Organizations having large data sets of spatial data need to do certain operations that …
Published in Journal of Operating Systems Development & Trends · Vol. 1, Issue 1, 2014 · pp. 21–28 Read article
-
Enhanced Incremental DB Scan Algorithm using Map-Reduce
Abstract: Distributed data mining is more efficient, scalable and its performance is better than the central data mining techniques. Incremental DBSCAN algorithm is better than the other method DBSCAN. Incremental DBSCAN can give better performance in distributed environment in terms of run time complexity using Hadoop platform. The proposed system uses spatial dataset i.e. dataset are distributed among different sites. Initially, clusters are generated at each site using DBSCAN algorithm. Then …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 3, Issue 2, 2015 · pp. 1–6 Read article