scholarly journals Identifying Residential Consumption Patterns Using Data-Mining Techniques: A Large-Scale Study of Smart Meter Data in Chengdu, China

2021 ◽  
Author(s):  
Jieyi Kang ◽  
David Reiner
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Consumption Patterns ◽  
Smart Meter ◽  
Data Mining Techniques ◽  
Large Scale Study ◽  
Using Data ◽  
Residential Consumption

Data Mining Prospects in Mobile Social Networks

2013 ◽  
pp. 145-172 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Zhongshi He ◽  
Huazheng Zhu
Keyword(s):  
Data Mining ◽  
Social Networking ◽  
Large Scale ◽  
New Technologies ◽  
Data Mining Techniques ◽  
Large Scale Data ◽  
Social Technologies ◽  
New Ideas ◽  
Using Data ◽  
Fundamental Insight

Unable to accommodating new technologies, including social technology, mobile devices and computing are other potential problems, which are significant challenges to social-networking service. The very broad range of such social-networking challenges and problems are demanding advanced and dynamic tools. Therefore, in this chapter, we introduced and discussed data mining prospects to overcome the traditional social-networking challenges and problems, which led to optimization of MSNs application and performances. The proposed method infers defining and investigating social-networking problems using data mining techniques and algorithms based on the large-scale data. The approach is also exploring the possible potential of users and systems contexts, which leads to mine the personal contexts such as the users’ locations and situations from the mobile logs. In these sections, we discussed and introduced new ideas on social technologies, data mining techniques and algorithm’s prospects, social technology’s key functional and performances, which include social analysis, security and fraud detections by presenting a brief analysis, and modeling based descriptions. The approach also empirically discussed using the real survey data, which the result showed how data mining vitally significant to explore MSNs performance and its crosscutting impacts. Finally, this chapter provides fundamental insight to researchers and practitioners who need to know data mining prospects and techniques to analyze large, complex and frequently changing data. This chapter is also providing a state-of-the-art of data mining techniques and algorithm’s dynamic prospects.

An Efficient Way of Handling Large Scale Web News Data using Data Mining Techniques

2019 ◽  
Author(s):  
Kratika Verma Verma ◽  
Anil Kumar Malviya
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Data Mining Techniques ◽  
Using Data ◽  
Web News

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Measuring the Efficiency of Non Technical Loss in Power Utilities using Data Mining Techniques

2014 ◽  
Vol 2 (2) ◽  
pp. 19-24
Author(s):  
Rajesh . ◽  
◽  
Siva Sankari ◽  
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Power Utilities ◽  
Using Data

Analytical Study and Prediction of Consumer Price Index for Rwanda Using Data Mining Techniques

2019 ◽  
Vol 9 (11) ◽  
pp. p95117
Author(s):  
Pascal Sindayiheba ◽  
Jean De Dieu Rwamasunzu ◽  
Papias Niyigena
Keyword(s):  
Data Mining ◽  
Analytical Study ◽  
Price Index ◽  
Consumer Price Index ◽  
Data Mining Techniques ◽  
Using Data

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

An Insight of Thyroid Disease Prediction using Data Mining Techniques

2020 ◽  
Author(s):  
Yasir Iqbal Mir ◽  
Sonu Mittal ◽  
Mirza Shuja
Keyword(s):  
Data Mining ◽  
Thyroid Disease ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Using Data
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