Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/334195
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dc.coverage.spatialInvestigations on data mining using soft computing techniques
dc.date.accessioned2021-08-02T04:27:06Z-
dc.date.available2021-08-02T04:27:06Z-
dc.identifier.urihttp://hdl.handle.net/10603/334195-
dc.description.abstractThere has been a great deal of revolution in the field of information processing and the communication sector with the advent of state of the art and cutting edge data acquisition and processing methods. Technological advancements in the field of data gathering have given birth to concepts of big data, which are mostly concerned with high-resolution data obtained from real-time environments. Real-time data are quite useful in information processes over their stored datasets as they best simulate the real-time scenario. In recent times, data analysis of these kinds of information obtained from the real-time scenario has dramatically aided in providing valuable and meaningful information that could be useful for a host of multi time critical applications. This process of extraction of meaningful information from a given dataset is termed as the knowledge discovery in databases and alternately termed as data mining. Data mining is observed to be attracting widespread research significance in recent times due to the exhaustive nature of data available in nature as well as for prediction applications. Extensive information extracted through the Internet of Things (IoT) has high business value in the ever-growing day-to-day information technology. In the ever-growing IoT scenario, almost all sub-sects related to it are easily controlled, monitored, notified, and communicated with one another by the rest of the objects. There are a large number of analysis based techniques integrated with IoT, which make it smarter and shrewd. Data mining is one of the technologies linked with the IoT era. newline
dc.format.extentxviii, 121p.
dc.languageEnglish
dc.relationp.113-120
dc.rightsuniversity
dc.titleInvestigations on data mining using soft computing techniques
dc.title.alternative
dc.creator.researcherSelvi S
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keyworddata mining
dc.subject.keywordsoft computing
dc.description.note
dc.contributor.guideChandrasekaran M
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File96.2 kBAdobe PDFView/Open
02_certificates.pdf208.52 kBAdobe PDFView/Open
03_vivaproceedings.pdf405.51 kBAdobe PDFView/Open
04_bonafidecertificate.pdf263.51 kBAdobe PDFView/Open
05_abstracts.pdf293.26 kBAdobe PDFView/Open
06_acknowledgements.pdf270.86 kBAdobe PDFView/Open
07_contents.pdf347.05 kBAdobe PDFView/Open
08_listoftables.pdf292.66 kBAdobe PDFView/Open
09_listoffigures.pdf423.79 kBAdobe PDFView/Open
10_listofabbreviations.pdf679.15 kBAdobe PDFView/Open
11_chapter1.pdf782.73 kBAdobe PDFView/Open
12_chapter2.pdf429.06 kBAdobe PDFView/Open
13_chapter3.pdf1.2 MBAdobe PDFView/Open
14_chapter4.pdf936.19 kBAdobe PDFView/Open
15_chapter5.pdf665.51 kBAdobe PDFView/Open
16_conclusion.pdf254.06 kBAdobe PDFView/Open
17_references.pdf367.67 kBAdobe PDFView/Open
18_listofpublications.pdf338.54 kBAdobe PDFView/Open
80_recommendation.pdf80.27 kBAdobe PDFView/Open


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