Classification Techniques and Data Mining Tools Used in Medical Bioinformatics

2019 ◽  
pp. 105-126
Author(s):  
Satish Kumar David ◽  
Amr T. M. Saeb ◽  
Mohamed Rafiullah ◽  
Khalid Rubeaan
Keyword(s):  
Data Mining ◽  
Genomic Analysis ◽  
Resistance Pattern ◽  
Medical Applications ◽  
Classification Analysis ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Drug Resistance Pattern ◽  
Mining Tools

Increasing volumes of data with the increased availability information mandates the use of data mining techniques in order to gather useful information from the datasets. In this chapter, data mining techniques are described with a special emphasis on classification techniques as one important supervised learning technique. Bioinformatics tools in the field for medical applications especially in medical microbiology are discussed. This chapter presents WEKA software as a tool of choice to perform classification analysis for different kinds of available data. Uses of WEKA data mining tools for biological applications such as genomic analysis and for medical applications such as diabetes are discussed. Data mining offers novel tools for medical applications for infectious diseases; it can help in identifying the pathogen and analyzing the drug resistance pattern. For non-communicable diseases such as diabetes, it provides excellent data analysis options for analyzing large volumes of data from many clinical studies.

A structured model for evaluating countermeasures at highway–railway grade crossings

2005 ◽  
Vol 32 (4) ◽  
pp. 627-635 ◽  
Author(s):  
Young-Jin Park ◽  
Frank F Saccomanno
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Expected Number ◽  
Structured Model ◽  
Support Tool ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Grade Crossings

Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highway–railway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highway–railway grade crossing, collision prediction model, countermeasures, Poisson regression.

scholarly journals Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients

2013 ◽  
Vol 19 (2) ◽  
pp. 121 ◽  
Author(s):  
Peyman Rezaei Hachesu ◽  
Maryam Ahmadi ◽  
Somayyeh Alizadeh ◽  
Farahnaz Sadoughi
Keyword(s):  
Data Mining ◽  
Length Of Stay ◽  
Cardiac Patients ◽  
Data Mining Techniques ◽  
Use Of Data

Applications of Data Mining in Software Development Life Cycle

2014 ◽  
pp. 67-79
Author(s):  
Naveen Dahiya ◽  
Vishal Bhatnagar ◽  
Manjeet Singh ◽  
Neeti Sangwan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Software Development ◽  
Future Research ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Development Life Cycle ◽  
Efficient Information ◽  
Software Development Life Cycle ◽  
Effective Use

Data mining has proven to be an important technique in terms of efficient information extraction, classification, clustering, and prediction of future trends from a database. The valuable properties of data mining have been put to use in many applications. One such application is Software Development Life Cycle (SDLC), where effective use of data mining techniques has been made by researchers. An exhaustive survey on application of data mining in SDLC has not been done in the past. In this chapter, the authors carry out an in-depth survey of existing literature focused towards application of data mining in SDLC and propose a framework that will classify the work done by various researchers in identification of prominent data mining techniques used in various phases of SDLC and pave the way for future research in the emerging area of data mining in SDLC.

Application of Data Mining Techniques in Weather Forecasting

2019 ◽  
pp. 162-174 ◽  
Author(s):  
ThippaReddy Gadekallu ◽  
Bushra Kidwai ◽  
Saksham Sharma ◽  
Rishabh Pareek ◽  
Sudheer Karnam
Keyword(s):  
Data Mining ◽  
Performance Metrics ◽  
Weather Forecasting ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
K Nearest Neighbors ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
The City ◽  
Case Data

Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this chapter, the authors investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation, and wind speed. This was carried out using artificial decision tree, naive Bayes, random forest, K-nearest neighbors (IBk) algorithms, and meteorological data collected between 2013 and 2014 from the city of Delhi. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. The results show that given enough case data, data mining techniques can be used for weather forecasting and climate change studies.

A Classification Framework for Data Mining Applications in Criminal Science and Investigations

2020 ◽  
pp. 277-293
Author(s):  
Mahima Goyal ◽  
Vishal Bhatnagar ◽  
Arushi Jain
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Mining Techniques ◽  
Road Map ◽  
Classification Framework ◽  
Challenges And Opportunities ◽  
Crucial Information ◽  
Tools And Techniques ◽  
Day By Day ◽  
Mining Tools

The importance of data analysis across different domains is growing day by day. This is evident in the fact that crucial information is retrieved through data analysis, using different available tools. The usage of data mining as a tool to uncover the nuggets of critical and crucial information is evident in modern day scenarios. This chapter presents a discussion on the usage of data mining tools and techniques in the area of criminal science and investigations. The application of data mining techniques in criminal science help in understanding the criminal psychology and consequently provides insight into effective measures to curb crime. This chapter provides a state-of-the-art report on the research conducted in this domain of interest by using a classification scheme and providing a road map on the usage of various data mining tools and techniques. Furthermore, the challenges and opportunities in the application of data mining techniques in criminal investigation is explored and detailed in this chapter.

Improved Privacy

2011 ◽  
pp. 295-316
Author(s):  
K. Abumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Empirical Analysis ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision-making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

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