Mehta Solutions Fundamentals of Data Mining M D U UNIVERSITY Fundamentals of Data Mining      .. Product #: 20IMG23GB2 Regular price: Rs600 Rs600

Fundamentals of Data Mining

Product Code: 20IMG23GB2
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Fundamentals of Data Mining  SOLVED PAPERS AND GUESS 

 

Product Details: M D U UNIVERSITY Fundamentals of Data Mining 

FormatBOOK 

Pub. DateNEW EDITION APPLICABLE FOR Current EXAM

PublisherMEHTA SOLUTIONS

Edition Description: 2021-22

RATING OF BOOK: EXCELLENT

  

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  If you find yourself getting fed up and frustrated with other M D U UNIVERSITY book solutions now mehta solutions brings top solutions for M D U UNIVERSITY Fundamentals of Data Mining   REPORT book contains previous year solved papers plus faculty important questions and answers specially for M D U UNIVERSITY.questions and answers are specially design specially for M D U UNIVERSITY students .

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Fundamentals of Data Mining 

UNIT-I
Introduction to Data Mining: basic concepts in data mining, machine learning, scientific methods, theoretical basis
of data mining process, data measurement, exploratory data analysis, data visualization, measurement of data
similarity and dissimilarity.
UNIT-II
Data Pre-processing: overview, data cleaning, data integration, data reduction, data transformation and data
discretization; Data Warehouse and Online Analytics Processing: data warehouse, data cube and OLAP, data
warehouse design and usage; Data Cube Technology- data cube computation, and its methods.
UNIT-III
Principles of Data Mining: predictive modelling- classification and regression, model fitting as optimization,
evaluation of predictive performance, over fitting, regularization; clustering and pattern detection.
UNIT-IV
Text Mining: information retrieval and search, text classification, unsupervised learning; Web Data Analysis: Web
data- collection and interpretation, analysing user browsing Behaviour, learning from click through data,
predictive modelling and online advertising, link analysis and the PageRank algorithm. Social Network Analysis:
descriptive analysis of social networks, network embedding and latent space models, network data over time:
dynamics and event-based networks link prediction.


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