PhD Topics in Computer Science Data Mining

PhD Topics in Computer Science Data Mining

      PhD Topics in Computer Science Data Mining is your definitive solution for all your research related issues. When it comes to Computer Science Data Mining, we suggest choosing Weka for data mining as it is platform-independent and possesses language portability, i.e., Java. Topics in Data Mining are an attractive field because of their growing relevance. We have the best team made up of vibrant experts and qualified developers who are working on the development of Data mining tools for more than a decade. Usually, scholars prefer projects using Data Mining. We can mine the best possible novel and an original concept for PhD Computer Science Data Mining.

Topics in Computer Science Data Mining

      PhD Topics in Computer Science Data Mining will remove all your stress and it will help you also to explore the field of data mining along with some aid from GUI environment. Data mining is also often considered an interdisciplinary field that falls under various domains such as Statistics, databases, Machine learning, Mathematics, visualization, and High-Performance Computing. Among various other tools, Weka can be considered the greatest tool that can also execute data mining concept, which has inbuilt data pre-processing tools and learning algorithms. We guide researchers and scholars from around the world to thoroughly explore this domain. Now let us also have a sneak peek regarding Computer Science Data Mining.

PhD Topics in Computer Science Data Mining Online

Important features of Data Mining

  • Holistic collection of Modeling and also data processing techniques
  • Platform supported are also Windows, MAC OS X, Linux
  • Execute various data mining operations such as data processing, classification, clustering, regression, feature selection and also visualization.
  • Possess features also for adding up new algorithms
  • Database connectivity using SQL
  • Grants a variety of algorithms for Data mining and also Machine learning approach
  • Open source and also Platform independent as it is written in Java
  • No need also for data mining specialist for handling it and also Provides flexibility for scripting
  • GUI Interfacing makes it user friendly
  •  Used along with R, Eclipse IDE, Matlab and also many more
  • Primarily used also for research and also educational purpose
It’s Objective:
  • Associative rule to associate data
  • Calculating methods
  • Clustering data
  • Categorization of data
  • Regression analysis and also prediction
  • Implementing Learning algorithms

Required Algorithms

Classification of Algorithm:
  • Artificial Immune Recognition system
  • Immuno-81
  • Clonal selection algorithm
  • Self organizing Map
  • Learning Vector Quantization
  • Feed forward also Artificial Neural Network
  • Recurrent Neural Networks
  • Deep Belief Networks
  • Deep Convolutional also Networks
Clustering Algorithms:
  • Density based spatial clustering algorithm
  • Cobweb Clustering algorithm
  • K-Means clustering
  • EM(also Expectation maximization)
  • Farthest first algorithm
  • Ordering points also to identify clustering structure(OPTICS)
  • Possibilistic C Means Algorithm
  • FCM, FPCM and also SPCM
Machine Learning Algorithms:
  • Deep Learning
  • Reinforcement learning
  • Decision tree learning
  • Bayesian networks
  • Artificial neural networks
  • Genetic algorithm
  • Association rule learning
  • Support vector machines
  • Inductive also  in logic programming
  • Sparse dictionary learning
  • Dynamic programming
  • Soft computing
  • Meta learning
  • Grammar Induction
  • Computational Learning Theory
Regression Algorithms:
  • Ordinary Least squares regression
  • Multivariate Adaptive Regression splines
  • Generalized Linear Models
  • Logistic and also Stepwise Regression
  • Locally Estimated also in Scatter plot smoothing

Accessible Datasets

Sample Datasets:
  • Biomedical dataset
  • Artificial and also real datasets
  • Protein dataset
  • Epitope Database
  • Agricultural datasets
  • Classification and also regression dataset
  • UCC and UCC KDD dataset
  • Datasets for all process
GUI Interface:
  • Build classifier
  • Data visualization
  • Cluster data and also find association
  • Pre-process data
  • Attribute selection
  • Comparison analysis of different learning schemes
Knowledge Flow:
  • Aids in the  process of setting up and also running machine learning experiments
  • Java beans based Interface
Research applications to investigate:
  • Health care applications
  • Temporal data mining approach
  • Analysis and also prediction of students behavior
  • Sentiment analysis i.e. opinion mining
  • Semantic and also bio data mining etc
  • Emotion analysis
  • Sequence mining
  • Network Intrusion detection also using data mining concepts
  • Teacher evaluation system
  • Business applications [also Amazon, Flipkart etc.]
  • Fraud detection
  • Intrusion detection
  • Lie detection
  • Customer segmentation

        The above-presented content in relation to the Computer Science Data Mining tool must also have cleared all your doubts about the subject matter. If you need more information regarding our service, feel free to email us anytime, and we will immediately also get back to you through our online guidance and tutoring.

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