PhD Guidance in Data Mining

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.

PhD Guidance in Data Mining Online Help 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
  • Tanagra
  • JHep Work
  • Angoss
  • Pentaho
  • Statistica (statsoft)
  • Oracle

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
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