Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/221067
Title: Big Data and Big Data Analytics Study and Analysis in Context to Data Clustering
Researcher: GIANEY HEMANT KUMAR
Guide(s): MEENU DAVE
Keywords: Engineering and Technology
University: Jagannath University
Completed Date: 2017
Abstract: In the recent decade Big Data has attracted attention from decision and policy makers in newlineenterprises and governments, market analysts, and data scientists. The growth of information newlinein the current decade has exceeded the Moore s law, and the vast amount of data is increasing newlinethe pain towards managing and analyzing. However, this high amount of data has a great newlinepotential and extremely useful information is hidden in it. Data-intensive scientific discovery newlinehelps to identify Big Data problems. The Big Data problems are found in various areas and newlinesectors such as economic activities to provide effective public administration, national newlinesecurity, and scientific research. Several progressions in various fields were made possible newlinebecause of Big Data and there is no doubt that the future challenges in business newlineenhancements will converge to explore Big Data. newlineThe era of huge data is snowballing at frequent swiftness in size (volume) and in different newlineformats (variety). This data which comes from various sources e.g. media, communication newlinedevices, internet, business etc. and there are many difficulties and challenges that one faces newlinewhile handling it. Data mining is a process intended to reconnoiter analytical data (typically newlinebusiness or market associated data - also acknowledged as quotBig Dataquot). There are several data newlinemining techniques such as outlier analysis, organization, clustering, prediction and newlineassociation rule mining. In this research several applications and the importance of clustering newlineis discussed. To examine the huge volume of data, clustering algorithms aid in providing a newlinepowerful meta-learning tool. Numerous clustering techniques (including traditional and the newlinerecently developed) in reference to large data sets with their pros and cons are being discussed newlinein this research. newlineClustering techniques are widely used in different field like image processing, data mining newlineetc. for finding different new patterns in underlying data. Cluster techniques are concerned newlinewith developing algorithm that is proven to be very usefu
Pagination: 
URI: http://hdl.handle.net/10603/221067
Appears in Departments:Faculty of Engineering and Technology

Files in This Item:
File Description SizeFormat 
01_title page.pdfAttached File79.5 kBAdobe PDFView/Open
02_certificate.pdf64.31 kBAdobe PDFView/Open
03_acknowledgment.pdf47.01 kBAdobe PDFView/Open
04_preface.pdf49.19 kBAdobe PDFView/Open
05_table of contents.pdf83.04 kBAdobe PDFView/Open
06_list of figures.pdf52.8 kBAdobe PDFView/Open
07_list of tables.pdf42.55 kBAdobe PDFView/Open
08_chapter 1.pdf387.04 kBAdobe PDFView/Open
09_chapter 2.pdf574.74 kBAdobe PDFView/Open
10_chapter 3.pdf434.82 kBAdobe PDFView/Open
11_chapter 4.pdf87.92 kBAdobe PDFView/Open
12_chapter 5.pdf121.75 kBAdobe PDFView/Open
13_chapter 6.pdf1.48 MBAdobe PDFView/Open
14_chapter 7.pdf55.68 kBAdobe PDFView/Open
15_appendices.pdf3.29 MBAdobe PDFView/Open
final_plagirism_26_07_2017.pdf22.1 MBAdobe PDFView/Open
metadata for cd.pdf69.22 kBAdobe PDFView/Open


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