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.

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

Experimenter:

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.