Application of Data Mining Techniques in Weather Forecasting

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.

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A structured model for evaluating countermeasures at highwayrailway grade crossings

Canadian Journal of Civil Engineering ◽

10.1139/l05-017 ◽

2005 ◽

Vol 32 (4) ◽

pp. 627-635 ◽

Cited By ~ 3

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 highwayrailway 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 highwayrailway 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: highwayrailway grade crossing, collision prediction model, countermeasures, Poisson regression.

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

Healthcare Informatics Research ◽

10.4258/hir.2013.19.2.121 ◽

2013 ◽

Vol 19 (2) ◽

pp. 121 ◽

Cited By ~ 76

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

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Applications of Data Mining in Software Development Life Cycle

Data Mining and Analysis in the Engineering Field - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-6086-1.ch004 ◽

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.

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Web Usage Mining Issues in Big Data

Impacts and Challenges of Cloud Business Intelligence - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

10.4018/978-1-7998-5040-3.ch007 ◽

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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