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AKTU Biological Data Mining (HTCB401) Syllabus PDF – B.Tech Honors Degree in Computational Biology and Bioinformatics
Biological Data Mining HTCB401
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Official AKTU syllabus of Biological Data Mining (HTCB401) for B.Tech Honors Degree in Computational Biology and Bioinformatics – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Biological Data Mining
Download PDFHTCB401 · 4 credits · L-T-P 3-1-0
Course Outcomes
| Course Outcome | Description At the end of the course, the student will be able to |
| CO1 | Study about construction of data warehouse, multidimensional data modeling, OLAP & OLTP technologies. |
| CO2 | Understand concepts of data mining and KDD process and various mining tasks. |
| CO3 | Apply mining association rules in large databases. |
| CO4 | Understand and apply classification & prediction methods. |
| CO5 | Understand and apply clustering methods. |
| Detailed Syllabus (Theory) | |
| Unit No. | Topics CO No. No. of proposed lectures |
| I | Data Warehousing: Components of Data warehousing; Need, Building and Interpretation of data warehouse, Data Mart, Multi Dimensional Data Model and related schemas, Data warehouse and OLAP technology, Online Analytical Processing (OLAP) - OLAP Vs OLTP, different OLAP operations. 1 8 |
| II | Data Mining: Basic concept, Data Collection, Types of data, KDD process, Data preprocessing, Outlier detection, Data integration, Data transformation, Data reduction, Data generation, Data summarization, Data presentation, Data Mining functionalities, Classification and architecture of data mining systems, Data Mining query language, Data mining task primitives, Integration of a data mining system with a data warehouse. 2 8 |
| III | Mining Association Rules in large databases: Association rule mining, Mining methods, Mining Multi Level Association Rule Mining, Multi Dimensional Association Rule, Mining Correlation analysis, Constraint based association mining, multidimensional association rules from relational DBS and DWS, Correlation analysis, Constraint based association mining, Mining frequent patterns, Mining various kinds of association rules. 3 8 |
| IV | Classification and Prediction: Classification methods: Decision tree induction, Artificial neural network, Baye’s Theorem, Bayesian classification and Bayesian Belief Network, Support vector machines, rule based classification, association based classification. Prediction: Linear & Non Linear Regression, Issues Regarding Classification and Prediction. 4 8 |
| V | Clustering: Cluster analysis, Types of clustering, partitioning method, hierarchical method, k means method, self optimization method, density based methods, grid based methods, text mining, web mining, temporal and spatial data mining. | 5 | 8 |
Text Books
- Data Mining Concepts & Techniques by Jiawei Han & Micheline Kamber; Harcourt India Pvt Ltd.
- Data warehousing by S . Anahory and D.Murray, Pearson Education, ASIA.
- Data Mining Techniques by A.K. Pujari , University Press.
Reference Books
- Building Data Ware House by W.H.Inmon, John Wiley & Sons.
- Data Ware Housing with oracle by SimaYazdanri&Shirky S. Wong.
- Data Mining, Practical Machine Cearing tools & techniques with Java by I.H. Whiffen.
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