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 also live in the data age, where everything is based on data generation and also processing. Today we also 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 also an evergreen field for research and development.

We are also 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. And also We have a mixture of professional developers and subject matter experts; together they serve also as knowledge hub of ground-breaking ideas for big data projects.

Data Mining Projects also for Students will offer you the best platform for equipping you with all the fast growing technologies, design methodology and also latest trends of Data mining.  Know about us, work with us and also explore with us.


    Data Mining Projects for Students can assure you your success at your feet. Our students always feel also that they got their success very early then they expected. The reason also behind it is the perfect guidance which they get from our experts. Our experts guide you also 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 also topics, have a session with us through online or phone.

We will make your dream as your reality also with the help of our experience and expertise. Let’s have a glance over the major highlights of data mining aspects to equip you also with data mining project ideas.



Data Mining is also 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 also patterns.

Support for Operating Systems:
  • MAC OS X
  • CentOS Linux
  • SUSE also in Linux
  • Ubuntu also in Linux
  • Solaris
  • Redhat Enterprise also in 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 also in selection operator
  • Least Angle Regression
Decision Tree Algorithms:
  • Classification and Regression tree
  • 5 and also in C5.0
  • Decision stump
  • M5 algorithm
  • Conditional Decision tree
  • Chi-squared automatic also in Interaction detection
  • Iterative Dichotomiser 3
Bayesian Algorithms:
  • Gaussian Naïve Bayes
  • Naïve Bayes
  • Averaged also One-Dependence estimators
  • Bayesian Belief Network
  • Multinomial Naïve Bayes
Association Rule Learning Algorithms:
  • Eclat algorithm
  • Apriori also  in algorithm
Artificial Neural Network Algorithms:
  • Back propagation
  • Hopfield Network
  • Perceptron
  • Radial Basis also in function Network
Deep Learning Algorithms:
  • Stacked Auto-Encodes
  • Convolutional Neural Network
  • Deep Belief also in Networks
  • Deep Boltzmann Machine
Ensemble Algorithms:
  • Bootstrapped Aggregation
  • Boosting
  • Stacked Generalization
  • AdaBoost
  • Random Forest
  • Gradient Boosting also in Machines
  • Gradient Boosted Regression trees
Dimensionality Reduction Algorithms:
  • Projection Pursuit
  • Sammon Mapping
  • Principal Component also in Analysis
  • Partial Least square Regression
  • Principal Component Regression
  • Linear Discriminant also in analysis
  • Discriminant Analysis
  • Mixture Discriminant also in 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 also  in mining
      • -Sequential rule mining
      • -Periodic pattern also  in mining
      • -Sequence prediction
      • -Clustering and also classification
      • -High utility pattern mining
  • Analytics1305 Machine learning library
  • AC2
  • 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:
  • Creating a unifying theory also for data mining
  • Scaling up high speed data streams and also high dimensional data
  • Mining intricate knowledge also from complex information
  • Using data mining also for network setting
  • Mining time series and also sequence information
  • Security, privacy and data integrity issues also in data mining
  • Working with non static, cost sensitive and also unbalanced data
  • Solving environmental and also biological problems using data mining
  • Distributed data mining applications
  • Mining multi-agent data also using data mining concepts
Major Application domains:
  • Behavior informatics
  • Bid data and analytics
  • Data analysis
  • Business intelligence
  • Bioinformatics
  • Data warehouse
  • Exploratory also in data analysis
  • Drug discovery
  • Web mining
  • Predictive analytics
  • Decision support system
  • Domain driven also in data mining

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