Research Paper, Computer sciences and Information technology

Data Mining and Knowledge Discovery in Database

Project description

In this project option, you will be expected to conduct a comprehensive literature search and survey,

select and study a specific topic in one subject area of data mining and KDD, and write a technical

paper on the selected topic all by yourself. The technical paper you are asked to write can be a

detailed comprehensive survey on some specific topic or the original research work that will have

been done by yourself.

Requirements and Instructions for the Technical Paper:

1. The objective of the paper should be very clear about subject, scope, domain, and the goals to be

achieved.

2. The paper should address the important advanced and critical issues in a specific area of data

mining and KDD. Your research paper should emphasize not only breadth of coverage, but also

depth of coverage in the specific area.

3. The research paper should give the measurable conclusions and future research directions (this is

your contribution).

4. It might be beneficial to review or browse through about 10 to 15 relevant technical articles

before you make decision on the topic of the research project.

5. The research paper should reflect the quality at certain academic research level.

7. The paper should include adequate abstraction or introduction, and reference list.

8. Please write the paper in your words and statements, and please give the names of references,

citations, and resources of reference materials if you want to use the statements from other

reference articles.

Suggested Topics for KDD Research (But not limited)

Theory and Fundamental Issues in KDD:

Data and knowledge representation for KDD

Database Models for knowledge discovery and data mining

Definitions, formalisms, and theoretical issues in KDD

Fundamental advances in search, retrieval, and discovery methods

Modeling of structured, unstructured and multimedia data for KDD

Metrics for evaluation of KDD results

Probabilistic modeling and uncertainty management in KDD

Data Mining Methods and Algorithms:

Algorithms for learning classification rules, characteristic rules, associative rules

Algorithms for association rule mining

Algorithms for clustering, predication, etc.

Algorithmic complexity, efficiency and scalability issues in KDD

High dimensional datasets and data preprocessing

Parallel and distributed data mining techniques

Probabilistic and statistical models and methods in KDD

Supervised and unsupervised discovery and predictive modeling

Using prior domain knowledge and re-use of discovered knowledge

Measurement of rule interestingness and quality

KDD Process and Human Interaction:

Models of the KDD process

Methods for evaluating subjective relevance and utility

Data and knowledge visualization

Interactive data exploration and discovery

Privacy preservation data mining and security

Applications:

Application of KDD in business, science, medicine and engineering

Application of KDD methods for mining knowledge in text, image,

audio, sensor, numeric, categorical or mixed format data, semi-structural data

Big-data mining and data analytics

Mining multimedia, hyper-text, spatial, temporal databases

Mining bioinformatics data

Applications of KDD for semantic query optimization

Knowledge discovery and data mining tools

Resource and knowledge discovery using the Internet

Suggested Check List for Written Report :

Your written report should try to include the following items:

a. Introduction and objectives of the research.

b. Current state of arts and existing methodologies in the specific area.

c. Barriers, issues, and open problems in the area.

d. Existing, expected, proposed solutions, methods, and algorithms if any at the time of the

project due.

e. Examples in details (step by step) to illustrate concepts, principles, theories, algorithms,

methodologies, etc.

e. Research results if any at the time of the report due.

f. Analysis and comparison of research methods, algorithms, and expected results if any at the

time of the report due.

g. Conclusions and future research directions if any at the time of the report due.

h. Reference list

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