A Comparative Study of Data Mining Tools and Techniques for Business Intelligence

2019 ◽  
pp. 163-173
Author(s):  
G. S. Ramesh ◽  
T. V. Rajini Kanth ◽  
D. Vasumathi
Keyword(s):  
Data Mining ◽  
Comparative Study ◽  
Business Intelligence ◽  
Tools And Techniques ◽  
Mining Tools

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.

Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework

2019 ◽  
pp. 460-477
Author(s):  
Satyadhyan Chickerur ◽  
Supreeth Sharma ◽  
Prashant M. Narayankar
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Business Intelligence ◽  
Quality Data ◽  
Agricultural Crops ◽  
Agricultural Community ◽  
Set Up ◽  
Efficiency And Productivity ◽  
Agricultural Demand ◽  
Mining Tools

Information technology is playing a very important role in all the spheres of life, starting from healthcare to entertainment. The agricultural community is not far behind in utilizing information technology for increasing the efficiency and productivity of agriculture and allied activities. This chapter proposes how the concepts of BI (business intelligence), BI tools, data mining tools might be used for forecasting the agricultural demand of various crops reliably and more efficiently. The chapter clearly elaborates how BI tools could be used during various stages of ETL (extract, transform, and load) and how cleansed, quality data could be used by data mining tools for forecasting. Experiments are carried out for forecasting the demands for various agricultural crops by using the previous year's demand, and the results are encouraging. The experimental set up involved open source tools like Pentaho's Kettle and Weka.

Data Mining Tools and Techniques for Identification of Biomarkers for Cancer

2009 ◽  
pp. 589-614
Author(s):  
Hai Hu ◽  
Robin Munro ◽  
Mick Correll ◽  
Jonathan Sheldon ◽  
Yike Guo ◽  
...  
Keyword(s):  
Data Mining ◽  
Tools And Techniques ◽  
Mining Tools

scholarly journals A Comprehensive Analysis of Proprietary and Open Source Data Mining Tools

2020 ◽  
pp. 414-420
Author(s):  
Sonia Rani Chowdhary ◽  
Mr Vikash
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Rapid Development ◽  
Time Analysis ◽  
Open Source Data ◽  
Source Data ◽  
Efficient Information ◽  
Tools And Techniques ◽  
Short Time ◽  
Mining Tools

The Powerful software tools and techniques required for the development of data mining applications. With the rapid development of technologies and business interest in using electronics and latest technologies plays important role in improvement of data mining field. Data mining access the meaningful and efficient information available in worldwide which is helps in decision making. This paper described the (a) various tools and techniques used by data mining applications. (b) compared features and limitations both in Proprietary and open sources data mining tools. (c) technical analysis of proprietary and open source data mining tools. On the basis of well-designed User interface, short time analysis, statistical and mathematical analysis user can select the best tool as per their requirements. Analysis of these tools makes easy to select appropriate tool.

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