Modern Technologies for Big Data Classification and Clustering

382 Pages·2017·5.397 MB·English

by  Hari Seetha, Hari Seetha, M. Narasimha Murty, B. K. Tripathy

Modern Technologies for
Big Data Classification
and Clustering
Hari Seetha
Vellore Institute of Technology-Andhra Pradesh, India
M. Narasimha Murty
Indian Institute of Science, India
B. K. Tripathy
VIT University, India
A volume in the Advances in Data
Mining and Database Management
(ADMDM) Book Series
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Library of Congress Cataloging-in-Publication Data
Names: Seetha, Hari, 1970- editor. | Murty, M. Narasimha, editor. | Tripathy,
B. K., 1957- editor.
Title: Modern technologies for big data classification and clustering / Hari
Seetha, M. Narasimha Murty, and B.K. Tripathy, editors.
Description: Hershey, PA : Information Science Reference, [2018]
Identifiers: LCCN 2017010783| ISBN 9781522528050 (hardcover) | ISBN
9781522528067 (ebook)
Subjects: LCSH: Big data. | Data mining. | Cluster analysis. |
Classification--Nonbook materials. | Document clustering.
Classification: LCC QA76.9.B45 M63 2018 | DDC 005.7--dc23 LC record available at https://lccn.
loc.gov/2017010783

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ISSN:2327-1981
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Table of Contents
Preface..................................................................................................................xii
Chapter 1
Uncertainty-Based.Clustering.Algorithms.for.Large.Data.Sets..............................1
B. K. Tripathy, VIT University, India
Hari Seetha, Vellore Institute of Technology – Andhra Pradesh, India
M. N. Murty, IISC Bangalore, India
Chapter 2
Sentiment.Mining.Approaches.for.Big.Data.Classification.and.Clustering.........34
Ashok Kumar J, Anna University, India
Abirami S, Anna University, India
Tina Esther Trueman, Anna University, India
Chapter 3
Data.Compaction.Techniques...............................................................................64
R. Raj Kumar, RGMCET, India
P. Viswanath, IIITS Chittoor, India
C. Shoba Bindu, JNTUA, India
Chapter 4
Methodologies.and.Technologies.to.Retrieve.Information.From.Text.Sources...99
Anu Singha, South Asian University, India
Phub Namgay, Sherubtse College, Royal University of Bhutan, Bhutan
Chapter 5
Twitter.Data.Analysis.........................................................................................124
Chitrakala S, Anna University, India

Chapter 6
Use.of.Social.Network.Analysis.in.Telecommunication.Domain......................152
Sushruta Mishra, KIIT University, India
Brojo Kishore Mishra, C. V. Raman College of Engineering, India
Hrudaya Kumar Tripathy, KIIT University, India
Monalisa Mishra, C. V. Raman College of Engineering, India
Bijayalaxmi Panda, BPUT Rourkela, India
Chapter 7
A.Review.on.Spatial.Big.Data.Analytics.and.Visualization...............................179
Bangaru Kamatchi Seethapathy, VIT University, India
Parvathi R, VIT University, India
Chapter 8
A.Survey.on.Overlapping.Communities.in.Large-Scale.Social.Networks.........198
S Rao Chintalapudi, JNTUK, India
H. M. Krishna Prasad M, JNTUK, India
Chapter 9
A.Brief.Study.of.Approaches.to.Text.Feature.Selection.....................................216
Ravindra Babu Tallamaraju, Flipkart Internet Private Limited, India
Manas Kirti, Flipkart Internet Private Limited, India
Chapter 10
Biological.Big.Data.Analysis.and.Visualization:.A.Survey...............................244
Vignesh U, VIT University, India
Parvathi R, VIT University, India
Related References............................................................................................260
Compilation of References...............................................................................297
About the Contributors....................................................................................353
Index...................................................................................................................358
Detailed Table of Contents
Preface..................................................................................................................xii
Chapter 1
Uncertainty-Based.Clustering.Algorithms.for.Large.Data.Sets..............................1
B. K. Tripathy, VIT University, India
Hari Seetha, Vellore Institute of Technology – Andhra Pradesh, India
M. N. Murty, IISC Bangalore, India
Data.clustering.plays.a.very.important.role.in.Data.mining,.machine.learning.and.
Image.processing.areas..As.modern.day.databases.have.inherent.uncertainties,.many.
uncertainty-based.data.clustering.algorithms.have.been.developed.in.this.direction..
These.algorithms.are.fuzzy.c-means,.rough.c-means,.intuitionistic.fuzzy.c-means.
and.the.means.like.rough.fuzzy.c-means,.rough.intuitionistic.fuzzy.c-means.which.
base.on.hybrid.models..Also,.we.find.many.variants.of.these.algorithms.which.
improve.them.in.different.directions.like.their.Kernelised.versions,.possibilistic.
versions,.and.possibilistic.Kernelised.versions..However,.all.the.above.algorithms.
are.not.effective.on.big.data.for.various.reasons..So,.researchers.have.been.trying.
for.the.past.few.years.to.improve.these.algorithms.in.order.they.can.be.applied.
to.cluster.big.data..The.algorithms.are.relatively.few.in.comparison.to.those.for.
datasets.of.reasonable.size..It.is.our.aim.in.this.chapter.to.present.the.uncertainty.
based.clustering.algorithms.developed.so.far.and.proposes.a.few.new.algorithms.
which.can.be.developed.further.
Chapter 2
Sentiment.Mining.Approaches.for.Big.Data.Classification.and.Clustering.........34
Ashok Kumar J, Anna University, India
Abirami S, Anna University, India
Tina Esther Trueman, Anna University, India
Sentiment.analysis.is.one.of.the.most.important.applications.in.the.field.of.text.
mining..It.computes.people’s.opinions,.comments,.posts,.reviews,.evaluations,.and.
emotions.which.are.expressed.on.products,.sales,.services,.individuals,.organizations,.

etc..Nowadays,.large.amounts.of.structured.and.unstructured.data.are.being.produced.
on.the.web..The.categorizing.and.grouping.of.these.data.become.a.real-world.
problem..In.this.chapter,.the.authors.address.the.current.research.in.this.field,.issues.
and.the.problem.of.sentiment.analysis.on.Big.Data.for.classification.and.clustering..
It.suggests.new.methods,.applications,.algorithm.extensions.of.classification.and.
clustering.and.software.tools.in.the.field.of.sentiment.analysis.
Chapter 3
Data.Compaction.Techniques...............................................................................64
R. Raj Kumar, RGMCET, India
P. Viswanath, IIITS Chittoor, India
C. Shoba Bindu, JNTUA, India
A.large.dataset.is.not.preferable.as.it.increases.computational.burden.on.the.methods.
operating.over.it..Given.the.Large.dataset,.it.is.always.interesting.that.whether.one.
can.generate.smaller.dataset.which.is.a.subset.or.a.set.(cardinality.should.be.less.
when.compare.to.original.dataset).of.extracted.patterns.from.that.large.dataset..The.
patterns.in.the.subset.are.representatives.of.the.patterns.in.the.original.dataset..The.
subset.(set).of.representing.patterns.forms.the.Prototype.set..Forming.Prototype.
set.is.broadly.categorized.into.two.types..1).Prototype.set.which.is.a.proper.subset.
of.original.dataset..2).Prototype.set.which.contains.patterns.extracted.by.using.the.
patterns.in.the.original.dataset..This.process.of.reducing.the.training.set.can.also.be.
done.with.the.features.of.the.training.set..The.authors.discuss.the.reduction.of.the.
datasets.in.the.both.directions..These.methods.are.well.known.as.Data.Compaction.
Techniques.
Chapter 4
Methodologies.and.Technologies.to.Retrieve.Information.From.Text.Sources...99
Anu Singha, South Asian University, India
Phub Namgay, Sherubtse College, Royal University of Bhutan, Bhutan
A.tool.which.algorithmically.traces.the.effectiveness.of.the.text.files.would.be.
helpful.in.determining.whether.the.text.file.have.all.the.characteristic.of.important.
concepts..Every.text.source.is.build.up.on.key.phrases,.and.these.paramount.phrases.
follow.a.certain.grammatical.linguistic.pattern.widely.used..An.enormous.amount.
of.information.can.be.derived.from.these.key.concepts.for.the.further.analysis.such.
as.their.dispersion,.relationship.among.the.concepts.etc..The.relationship.among.
the.key.concepts.can.be.used.to.draw.a.concept.graphs..So,.this.chapter.presents.
a.detailed.methodologies.and.technologies.which.evaluate.the.effectiveness.of.the.
extracted.information.from.text.files.

Chapter 5
Twitter.Data.Analysis.........................................................................................124
Chitrakala S, Anna University, India
Analyzing.Social.network.data.using.Big.Data.Tools.and.techniques.promises.to.
provide.information.that.could.be.of.use.in.recommendation.systems,.personalized.
service.and.many.other.applications..A.few.of.the.analytics.that.do.this.include.
sentiment.analysis,.trending.topic.analysis,.topic.modeling,.information.diffusion.
modeling,.provenance.determination.and.social.influence.study..Twitter.Data.
Analysis.involves.analyzing.data.specifically.obtained.from.Twitter,.both.tweets.
and.the.topology..There.are.three.major.classifications.on.the.type.of.analysis.being.
performed.such.as.Content.based,.Network.based.and.Hybrid.analysis..Trending.
Topic.Analysis.in.the.context.of.Content.based.static.data.analysis.and.Influence.
Maximization.in.the.context.of.Hybrid.analysis.on.data.streams.using.the.power.
of.Big.Data.Analytics.are.discussed..A.novel.solution.to.Trending.Topic.analysis.
to.generate.topic.evolved,.conflict-free.sequential.sub.summaries.and.influence.
maximization.to.handle.streaming.data.are.explained.with.experimental.results.
Chapter 6
Use.of.Social.Network.Analysis.in.Telecommunication.Domain......................152
Sushruta Mishra, KIIT University, India
Brojo Kishore Mishra, C. V. Raman College of Engineering, India
Hrudaya Kumar Tripathy, KIIT University, India
Monalisa Mishra, C. V. Raman College of Engineering, India
Bijayalaxmi Panda, BPUT Rourkela, India
Social.network.analysis.(SNA).is.the.analysis.of.social.communication.through.
network.and.graph.theory..In.our.chapter.the.application.of.SNA.has.been.explored.
in.telecommunication.domain..Telecom.data.consist.of.Customer.data.and.Call.
Detail.Data.(CDR)..The.proposed.work,.considers.the.attributes.of.call.detail.data.
and.customer.data.as.different.relationship.types.to.model.our.Multi-relational.
Telecommunication.social.network..Typical.work.on.social.network.analysis.includes.
the.discovery.of.group.of.customers.who.shares.similar.properties..A.new.challenge.
is.the.mining.of.hidden.communities.on.such.heterogeneous.social.networks,.to.group.
the.customers.as.churners.and.non-churners.in.Telecommunication.social.network..
After.the.analysis.of.the.available.data.we.constructed.a.Weights.Multi-relational.
Social.Network,.in.which.each.relation.carry.a.different.weight,.representing.how.
close.two.customers.are.with.one.another..The.centrality.measures.depict.the.intensity.
of.the.customer.closeness,.hence.we.can.determine.the.customer.who.influence.the.
other.customer.to.churn.