Data Mining / Image Mining / Cloud Computing

Data Mining/ Image Mining / Cloud Computing

Data mining is the process of mining patterns from big data sets by connecting approaches from statistics and artificial intelligence with database management. It is an interdisciplinary field of computer science. Data mining specialization courses edify data mining techniques for both structured and unstructured data. It includes the special types of courses including pattern discovery, text mining and analytics and data visualization. They are many course topics covered clustering, pattern discovery, text mining and analytics. This course extremely becomes popular and increasing importance among IT manager and data analyst to enhance their data management and analysis techniques. Particularly, professionals are doing the masters and doctorate in this interdisciplinary field in data mining program would accomplish professionally successful. Below list out the Table.1 some of the current Masters of Science data mining modules.

Table.1 Masters of Science in data mining program curriculum include

CORE COURSES (27 CREDITS)

ELECTIVE COURSES (6 CREDITS)

  • Introduction to Data Mining
  • Multivariate Analysis for Data Mining
  • Clustering and Affinity Analysis
  • Data Mining for Genomics and Proteomics
  • Predictive Analytics
  • Text Mining
  • Thesis

  • Applied Categorical Data Analysis
  • Artificial Intelligence: Machine Learning
  • Current Issues in Data Mining
  • Database Systems and Applications: Data Mining Experimental Design
  • Fundamentals of SAS
  • Nonparametric Statistics
  • Web Mining


Those professionals who have completed the masters will progress their advance research with the help of PhDs.

Below list of some of the potential research topics in PhDs Data mining

ALGORITHMS AND TECHNIQUES

DATA MINING AND DATABASES

FOUNDATIONS OF DATA MINING

INNOVATIVE APPLICATIONS

MINING DIFFERENT FORMS OF DATA

DATA PRE-PROCESSING

KDD PROCESS AND PROCESS-CENTRIC DATA MINING

PATTERN POST-PROCESSING

  • Statistical techniques and mixture models
  • Scalable algorithms
  • Rule discovery
  • Privacy preserving data mining
  • Multi-relational data mining
  • Incremental algorithms
  • Frequent patterns
  • Distributed and parallel algorithms
  • Constraint-based mining
  • Clustering
  • Classification

  • Data mining query languages and optimization
  • Database integration
  • Inductive databases

  • Statistical inference and probabilistic modeling
  • Logic for data mining
  • Knowledge (pattern) representation
  • Global vs. local patterns
  • Complexity issues

  • Web content, structure and usage mining
  • Semantic web mining
  • Personalization
  • Mining governmental data, mining for the public administration
  • Mining biomedical data
  • Invisible data mining
  • Adaptive data mining architectures

  • Text mining
  • Temporal, spatial, and spatiotemporal data mining
  • Semi-structured and XML data mining
  • Multimedia miningPattern post-processing
  • Graph, tree, sequence mining
  • Data stream mining

  • Dimensionality reduction
  • Data reduction
  • Discretization
  • Uncertain and missing information handling

  • Vertical data mining environments
  • Standards for the KDD process
  • Models of the KDD process
  • Collaborative data mining
  • Background knowledge integration

  • Visualization
  • Quality assessment
  • Knowledge interpretation and use

During this course of study, academic score plays a major role in completing the program. Particularly, most of the nations and universities PhDs dissertation and thesis/Master’s thesis score are an important one. Undoubtedly, the Ph.D. dissertation is not an easy writing task because it needed an extraordinary writing skill with proficiency and experience in that particular field as well as good reasoning and analytical skills to tackle this hard task. We are the one of the No.1 recommended and best assistant for any student’s research writing work. Eventually, this challenge worries that clearing the program as important as 50% as scoring dissertation/thesis to accomplish high credits in order to complete a research project.

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