DATA MINING PROJECTS FOR STUDENTS

 

 

 

DATA MINING PROJECTS FOR STUDENTS

    Data Mining Projects for Students, a universal development platform for students to upgrade the skills of students and make them shine in the midst of thousands. We live in the data age, where everything is based on data generation and processing. Today we get our required information from web within minutes, what makes it possible? It is based on the Web mining technology, a part of data mining, which extracts the required information from large set of information. This makes Data mining an evergreen field for research and development. We are working for the past 10 years on all the latest concepts, tools and technologies of data mining, which has made us a data hub of data mining project ideas. We have a mixture of professional developers and subject matter experts; together they serve as knowledge hub of ground-breaking ideas for big data projects. Data Mining Projects for Students will offer you the best platform for equipping you with all the fast growing technologies, design methodology and latest trends of Data mining.  Know about us, work with us and explore with us.

DATA MINING PROJECTS FOR STUDENTS

    Data Mining Projects for Students can assure you your success at your feet. Our students always feel that they got their success very early then they expected. The reason behind it is the perfect guidance which they get from our experts. Our experts guide you in the perfect path of success that will soon make you realize that you have touched the pinnacle of your success. To compile latest data mining project ideas and topics, have a session with us through online or phone. We will make your dream as your reality with the help of our experience and expertise. Let’s have a glance over the major highlights of data mining aspects to equip you with data mining project ideas.

KEY POINTS TO PONDER:

WHAT IS DATA MINING:

Data Mining is a non trivial extraction of large amount of previously unknown and potentially useful information from data, to analyze and explore the data to discover new trends and patterns.

Support for Operating Systems:

  • MAC OS X
  • CentOS Linux
  • SUSE Linux
  • Ubuntu Linux
  • Solaris
  • Redhat Enterprise Linux
  • Microsoft Windows

Programming languages and file extension:

  • R [.r,.R,.RData,.rds,.rda]
  • Python[.py,.pyc,.pyo,pyz,.pyw,pyd]
  • SQL[.sql]
  • Lisp[.lisp,.lsp]
  • Matlab[.m]
  • Java[.java]

Algorithms Used:

Clustering Algorithms:

  • K-Medians
  • K-Means
  • Expectation Maximization
  • Hierarchical Clustering

Regularization Algorithms:

  • Elastic Net
  • Ridge Regression
  • Least Absolute Shrinkage and selection operator
  • Least Angle Regression

Decision Tree Algorithms:

  • Classification and Regression tree
  • 5 and C5.0
  • Decision stump
  • M5 algorithm
  • Conditional Decision tree
  • Chi-squared automatic Interaction detection
  • Iterative Dichotomiser 3

Bayesian Algorithms:

  • Gaussian Naïve Bayes
  • Naïve Bayes
  • Averaged One-Dependence estimators
  • Bayesian Belief Network
  • Multinomial Naïve Bayes

Association Rule Learning Algorithms:

  • Eclat algorithm
  • Apriori algorithm

Artificial Neural Network Algorithms:

  • Back propagation
  • Hopfield Network
  • Perceptron
  • Radial Basis function Network

Deep Learning Algorithms:

  • Stacked Auto-Encodes
  • Convolutional Neural Network
  • Deep Belief Networks
  • Deep Boltzmann Machine

Ensemble Algorithms:

  • Bootstrapped Aggregation
  • Boosting
  • Stacked Generalization
  • AdaBoost
  • Random Forest
  • Gradient Boosting Machines
  • Gradient Boosted Regression trees

Dimensionality Reduction Algorithms:

  • Projection Pursuit
  • Sammon Mapping
  • Principal Component Analysis
  • Partial Least square Regression
  • Principal Component Regression
  • Linear Discriminant analysis
  • Discriminant Analysis
  • Mixture Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Multidimensional Scaling

Libraries and Frameworks Used:

  • SPMF(Open source data mining library in java)

Used for Pattern mining and offers 120 data mining algorithms for:

                  -Itemset Mining

                  -Sequential pattern mining

                 -Association rule mining

                 -Sequential rule mining

                 -Periodic pattern mining

                 -Sequence prediction

                 -Clustering and classification

                 -High utility pattern mining

  • Analytics1305 Machine learning library
  • AC2
  • GEPSR
  • IL Numerics
  • IMSL Numerical Libraries

GUI Interface and Database used:

GUI Interface support:

  • Weka(version 3.8)
  • Rattle GUI (Version 2.6.25)
  • Orange(Version 3.3.6)
  • Oracle Data Miner GUI(Version 4.0)
  • Rapid Miner(Version-RapidMiner Studio 7.1)

Databases Used:

  • Oracle Database 12c
  • SQL Server
  • Apache Mahout
  • Hive

MAJOR RESEARCH AREAS AND APPLICATIONS:

Major Research areas:

  • Creating a unifying theory for data mining
  • Scaling up high speed data streams and high dimensional data
  • Mining intricate knowledge from complex information
  • Using data mining for network setting
  • Mining time series and sequence information
  • Security, privacy and data integrity issues in data mining
  • Working with non static, cost sensitive and unbalanced data
  • Solving environmental and biological problems using data mining
  • Distributed data mining applications
  • Mining multi-agent data using data mining concepts

Major Application domains:

  • Behavior informatics
  • Bid data and analytics
  • Data analysis
  • Business intelligence
  • Bioinformatics
  • Data warehouse
  • Exploratory data analysis
  • Drug discovery
  • Web mining
  • Predictive analytics
  • Decision support system
  • Domain driven data mining

 

    Did you feel, you have got all the required information needed for your project? We have provided a complete gist of information required to take a project in data mining. If you feel, you need more information and guidance; you can have a session with us online. We will give you more informatics and mined information for your project.

DATA MINING PROJECTS FOR STUDENTS……………

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