DATA MINING PROJECTS USING WEKA

DATA MINING PROJECTS USING WEKA

      Data Mining Projects Using Weka will make you to reach your ultimate destination to touch the highest peak of research. Weka is actually an open source tool used for data mining purpose. Scholars and developers prefer Weka for data mining due to its platform independent feature and language portability (Java). Data Mining Projects Using Weka has its own significance in the field of research, which attracts majority of scholars and students towards it. We have dynamic experts and well trained developers working on Data mining tools and development for the past 10 years. Many projects can be taken using Data Mining concepts, but mining a novel concept for data mining project remains an issue. You can mine best and novel ideas with the help of our experts as our experts have a wide experience and knowledge regarding Data mining concepts.

DATA MINING PROJECTS USING WEKA

     Data Mining Projects Using Weka will give you an ease to work and explore the field of data mining with the help of its GUI environment. Data mining is an interdisciplinary field which involves Statistics, databases, Machine learning, Mathematics, Visualization and high performance computing. Weka is one of the best tool to implement data mining concept, which has inbuilt data pre processing tools and learning algorithms. Researchers and scholar can explore this domain with our guidance, to rock the grounds of Research. Let’s have an overall glance over the major concepts used in Weka and its research applications.

WEKA IN DATA MINING:

WEKA:

Weka(Waikato Environment for Knowledge Analysis):

  • Popular suite of machine learning algorithm used to solve real world data mining problems. Written in java and freely available under GNU General public license.

Key features of Weka(Latest version- Weka 3.8):

  • Provides various algorithms for Data mining and Machine learning approach
  • Open source and Platform independent(written in Java)
  • Doesn’t require data mining specialist to handle it and Provides flexibility for scripting
  • Has features for adding up new algorithms
  • Performs various data mining tasks like data processing, classification, clustering, regression, feature selection and visualization.
  • Holistic collection of Modeling and data processing techniques
  • GUI Interfacing makes it user friendly
  • Database connectivity using SQL(Using Java database connectivity and result is returned by database query)
  • Platform support(Windows, MAC OS X, Linux)
  • Can be used with R, Eclipse IDE, Matlab etc
  • Mainly used for research and educational purpose

Mainly used for:

  • Classification of data
  • Regression analysis and prediction
  • Clustering data
  • Associative rule to associate data
  • Implementing Learning algorithms
  • Evaluating methods

Major Algorithms Used:

Classification algorithm:

  • Self organizing Map
  • Learning Vector Quantization
  • Artificial Immune Recognition system
  • Feed forward Artificial Neural Network
  • Clonal selection algorithm
  • Immuno-81

Regression algorithms:

  • Generalized Linear Models
  • Logistic and Stepwise Regression
  • Multivariate Adaptive Regression splines
  • Ordinary Least squares regression
  • Locally Estimated Scatter plot smoothing

Clustering algorithm:

  • EM(Expectation maximization)
  • Farthest first algorithm
  • Ordering points to identify clustering structure(OPTICS)
  • Density based spatial clustering algorithm
  • K-Means clustering
  • Cobweb Clustering algorithm

Machine learning algorithms:

  • Decision tree learning
  • Artificial neural networks
  • Deep Learning
  • Association rule learning
  • Support vector machines
  • Inductive logic programming
  • Reinforcement learning
  • Genetic algorithm
  • Sparse dictionary learning
  • Bayesian networks

Datasets and data formats Available:

Use ARFF(Attribute Relation File format)

Sample Datasets Used:

  • Agricultural datasets
  • Classification and regression dataset
  • Protein dataset
  • Biomedical dataset
  • Epitope Database
  • UCC and UCC KDD dataset
  • Artificial and real datasets

GUI Interface support:

Explorer[exploratory data analysis]:

  • Pre-process data
  • Build classifier
  • Cluster data and find association
  • Attribute selection
  • Data visualization

Experimenter:

  • Comparison analysis of different learning schemes

KnowledgeFLow:

  • Java beans based Interface
  • Used to set up and run machine learning experiments

Simple Command liner:

Research applications to explore:

It is mainly used for education and research purpose. It is a tool for data mining application. Its major application in the field of data mining:

  • Sentiment analysis i.e opinion mining
  • Sequence mining
  • Analysis and prediction of students behavior
  • Network Intrusion detection using data mining concepts
  • Emotion analysis
  • Health care applications
  • Teacher evaluation system
  • Temporal data mining approach
  • Semantic and bio data mining etc

 

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