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). 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 also used to solve real world data mining problems. Written also in java and freely available under GNU General public license.
Key features of Weka(Latest version- Weka 3.8):
  • Provides various algorithms also 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 also Provides flexibility for scripting
  • Has features also for adding up new algorithms
  • Performs various data mining tasks like data processing, classification, clustering, regression, feature selection and also visualization.
  • Holistic collection of Modeling and also data processing techniques
  • GUI Interfacing makes it user friendly
  • Database connectivity using SQL(also Using Java database connectivity and result is returned by database query)
  • Platform support(also Windows, MAC OS X, Linux)
  • Can be used with R, Eclipse IDE, also Matlab etc
  • Mainly used for research and also educational purpose
Mainly used for:
  • Classification of data
  • Regression analysis and also prediction
  • Clustering data
  • Associative rule to associate data
  • Implementing Learning also algorithms
  • Evaluating methods

Major Algorithms Used

Classification algorithm:
  • Self organizing Map
  • Learning Vector Quantization
  • Artificial Immune Recognition system
  • Feed forward also Artificial Neural Network
  • Clonal selection algorithm
  • Immuno-81
Regression algorithms:
  • Generalized Linear also Models
  • Logistic and Stepwise Regression
  • Multivariate Adaptive also Regression splines
  • Ordinary Least squares regression
  • Locally Estimated also Scatter plot smoothing
Clustering algorithm:
  • EM(Expectation maximization)
  • Farthest first algorithm
  • Ordering points to identify clustering structure(also OPTICS)
  • Density based spatial also clustering algorithm
  • K-Means clustering
  • Cobweb Clustering also algorithm
Machine learning algorithms:
  • Decision tree learning
  • Artificial neural networks
  • Deep Learning
  • Association rule also in learning
  • Support vector machines
  • Inductive logic programming
  • Reinforcement also learning
  • Genetic algorithm
  • Sparse dictionary also in learning
  • Bayesian networks

Datasets and data formats Available:

Use ARFF(Attribute Relation File format)

Sample Datasets Used:
  • Agricultural datasets
  • Classification and also regression dataset
  • Protein dataset
  • Biomedical dataset
  • Epitope Database
  • UCC and UCC KDD dataset
  • Artificial and also real datasets
GUI Interface support:
Explorer[exploratory data analysis]:
  • Pre-process data
  • Build classifier
  • Cluster data and also find association
  • Attribute selection
  • Data visualization
Experimenter:
  • Comparison analysis of different learning schemes
KnowledgeFLow:
  • Java beans based Interface
  • Used to set up and also run machine learning experiments
Simple Command liner:
Research applications to explore:

It is mainly used for education and research purpose. It is also 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 also using data mining concepts
  • Emotion analysis
  • Health care applications
  • Teacher evaluation system
  • Temporal data mining also  in approach
  • Semantic and also bio data mining etc

 You may get an idea about Projects Using Weka tool by referring the above mentioned content. You can also have an in depth research in data mining concepts and issues, by getting an aid by our top experts. Click one mail to us; we will be back to you through also our online guidance and tutoring.

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