Ph.D Guidance In Data Mining

Data mining is a process that uses a variety of data analysis tools to discover patterns and Relation ships in data that may be used to make valid predictions. The newest answer to increase revenues and to reduce costs is data mining. The potential returns are enormous. Innovative organizations worldwide are already using data mining to locate and appeal to higher-value customers, to reconfigure their product offerings to increase sales, and to minimize losses due to error.

Data mining involves describing the data followed by summarizing its statistical attributes and visual review by using charts and graphs and then looking for potentially meaningful links among variables. Exploring and selecting the right data are critically important.

Data mining is still gaining momentum and the players are rapidly changing. Data mining is an evolving field, with great variety in terminology and methodology. Data mining is one of the most interesting project domains of S-LOGIX which will help the students in getting an efficient aerial view of this domain to put it into an effective project.

  • Research Proposal in Data Mining
  • Problem Identification in Data Mining
  • Research Methodology
  • Mathematical Model / Formulation
  • Literature Survey in Data Mining
  • System Design and Implementation
  • Performance Analysis and Results
  • Writing Services: Journal Papers, Synopsis and Thesis

Courses

  • Ph.D – Computer Science and Engineering, Computer Science, Computer Application, Information Technology, Communication Networks and Wireless Communication
  • M.E/M.Tech/M.S – Computer Science and Engineering, Computer Science, Computer Application,¬†Information Technology, Communication Networks and Wireless Communication.

Related Area

  • Mining Frequent Patterns, Associations, and Correlations
  • Association Rules and Sequential Patterns
  • DataWarehouse and OLAP
  • Classification and Clustering
  • Mining Sequence Patterns in Transactional Databases
  • Mining Data Streams
  • Mining Time-Series Data
  • Graph Mining
  • Social Network Analysis
  • Multirelational Data Mining
  • Spatial Data Mining
  • Multimedia Data Mining
  • Text Mining
  • Mining theWorld WideWeb

Tools and Technologies

  • Netbeans 7.0
  • Eclipse 4.3
  • Tomcat 7.0
  • Glass Fish 4.0
  • My-SQL 5.5
  • SQL-Server /MS-Access
  • Apache Axis2
  • Weka 3
  • Protege 3.4
  • ArgoUML
  • Java Database Connectivity (JDBC)
  • Swings
  • Collections
  • Java Networking
  • Java Server Pages (JSP)
  • Servlets
  • Java Media Framework (JMF)
  • Apache Jena – Jena Ontology
  • Java Data Mining