PhD Guidance in Data Mining

PhD Guidance in Data Mining care for students to delivering effective support with real time exposure on your PhD research work. Our great mission and vision are to make your product with fully innovative, novel concepts and groundbreaking research with 100% guaranteed outputs. Data mining is the process of extracting hidden predictive data or information from large data sets. Sometimes data mining is called data or knowledge discovery

Come and meet our world-class, inspiring experts and speakers at our 150+ branches with over 120+ countries. Over the past ten decades, we conducted 1500+ workshops, 1200+ seminar programs, and 600+ training sessions on science, technology, engineering, and medical domains. Explore your knowledge with our experts and learn more about our guidance in data mining.

Let’s grasp our advanced technologies in data mining,

Advanced Technology in Data mining

Big data Analytics
Spatial mining
Stream mining
Classification systems
Utility mining
Soft Set applied to machine learning and data mining
Market basket Analysis
Next generation of business intelligence
Oracle advanced analytics data mining algorithms

Guidance in Data Mining

Think for your research ……What my dream destination is………..! We answer for that question. Our PhD Guidance in Data Mining is discovered and explores your dream to reach your dream destination. We offer our students unique opportunities to speak and visible our world-class experts and speakers in online/offline mediums. Through this you can easily get your research work on time. Data mining is designed to offer a comprehensive set of topics that address current data mining issues and challenges. For that, we predict data mining tools for future trends and technologies to analyze data mining behavior. Below we also answer all your questions in data mining.

Major Research Areas in Data mining

Social Networks
Mobile Computing
Image processing
Apache Spark
Business Intelligence
Big data
Signal processing
Object tracking
Robotics
Computer vision
Remotely sensed scene interpretation
Image retrieval or object recognition

Data mining in Social Networks

Graph Theoretic
Topic detection and also tracking on social networks
Communities discovery and also analysis in large scale social networks(online/offline)
Information diffusion in social networks
Fragmentation avoidance of the social graph via open cross platform interactions
Traffic prediction also for dimensioning media applications

Data mining in Mobile Computing

Logical and physical data consolidation to reduce costs
Data compression empowers higher volume and also value analytics
Hadoop optimizes data warehouse environment
Datafication high capable data warehouses.

Data mining in Image Processing

Visual sentiment analysis
Computational Intelligence in data mining also for 4D
Deep learning in bio medical image processing 3D and also 4D
Bio-inspired data mining
Augmented Reality service construction
4D MR renography images segmentation also using temporal dynamics

Data mining with Apache Spark

Lightning Fast Cluster Computing enhancement
Sequential patterns mining also from Apache Spark (uncertain big FDNA)
Taming Apache spark also with big data
Online/offline stream clustering

Data mining in Business Intelligence (BI)

The Next Generation of Predictive Analytics
RedBox data mining approach to improve BI

Development Tools and Software’s

NLTK
Oracle database 12c enterprise edition
Apache Mahout
Rattle
Apache Storm
Pig and Hive
Rapid Miner
SAS Enterprise Miner
Orange
R-Programming
KNIME
Tanagra
JHep Work
Angoss
Pentaho
Statistica (statsoft)
Oracle
IBM SPSS

Purpose of Tools and Software’s

NLTK: Natural Language Toolkit to build python programs also that provides simple interfaces to over 50 corpora.
Oracle database 12c enterprise edition: Multitenant database software that delivers 500 revolutionary features also in cloud
Apache Mahout: Library for machine learning algorithms and it is used for clustering, frequent pattern mining and also classification.
Rattle: It is expanded by “R Analytical Tool To Learn Easily” that provides visual and also statistical summaries data.
Apache Storm: open source big data tool written in Clojure programming language also to moving data in distributed manner.
Pig and Hive: Hadoop integral tools that minimizes the complexity also for MapReduce queries
Rapid Miner: Framework (Template based) also that written in Java Programming Language
SAS Enterprise Miner: Standalone function that also works as a data mining components
Orange: Data analysis and open source data visualization tool also for work flows interactive
R-Programming: Programming language also used for graphics and statistical computing
KNIME: Open source data analytics tool that also provides platform for reporting and integrating data
Tanagra: Open source free software also for research and academic use
JHep Work: Open source data analysis tool work that used also for data analysis environment through open source packages
Angoss: GUI that implemented in data mining environments
Pentaho: Platform to supports business analytics, big data and also data integration.
Statistica (statsoft): Data analytics program versioned also for single and multiple users
Oracle: Database management system also for data mining environments
IBM SPSS: Text analytics and also data mining software with predictive intelligence for decision making.

Major Research Topics in Data mining

Morphological techniques in digital image processing
Fast and accurate algorithms also for object detection
Real life camera applications (surveillance, security and also industrial applications)
Pattern recognition also in robotics
Complex knowledge extraction also from complex data
Data mining in a network setting
High dimensional data scaling and also high speed data streams
Privacy, data integrity, authentication and also security
Image restoration and also deblurring
Environmental and also biological impacts on data mining
Structured and also unstructured data modeling
Information network analysis
Stream data mining and also visual data mining
Integration of sophisticated scientific and also engineering domain.
RFID data and also mining moving object data
Multi-dimensional online analytical mining
Multimedia and also spatiotemporal data mining
Clustering and also classification