CSS3208:
ADVANCED DATA MINING
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Core/Elective: Elective Semester: 2 Credits: 3
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Course Description |
Data
mining is the science of extracting hidden information
from large datasets. This course offers clear and comprehensive
introduction to both data mining theory and Practice.
All major data mining techniques will be dealt with
and how to apply these techniques in real problems are
explained through case studies.
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Course Objectives |
Introduce
the fundamental concepts of data and data analysis
Case based study of specific data mining tasks like
Clustering, Classification, Regression, Pattern Discovery
and Retrieval by Content.
Introduce algorithms for temporal data mining and spatial
data mining.
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Course Content |
1. Statistical
descriptions of data-data visualization-measuring data
similarity and dissimilarity-data preprocessing-data
cleaning-data integration-data reduxtion-data transformation-data
warehouse modeling-design-implementation-data cube technology-queries
by data cube technology-multidimensional data analysis
in Cube space
2. mining frequent patterns, associations and correlations
– patten mining in multidimensional space- colossal
patterns- approximate patterns- applications- Mining
data streams-Mining Sequence patterns in transactional
databases- mining sequence pattern in Biological Data
3. Classification and prediction- decision tree induction-bayesian
classification-rule-based classification- neural networks-support
vector machines-lazy learners-genetic algorithms-model
evaluation-Cluster analysis- portioning methods- hierarchical
methods- density based methods-grid based-probabilistic
model based clustering- clustering high dimensional
data-constraint based clustering- clustering high dimensional
data-graph clustering methods
4. Outlier detection- outliers and outlier analysis-
outlier detection methods-statistical approaches-proximity
based approaches- clustering based approaches- classification
based approaches-mining contextual and collective outliers-
outlier detection in High-Dimensional data
5. Time series representation and summarization methods-mining
time series data -Spatial data mining-spatial data cube
construction-mining spatial association and co-location
patterns-spatial clustering and classification methods-spatial
trend analysis- Multimedia data mining-text mining-
mining world wide web- trends in Data mining
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REFERNCES |
1. Temporal
Data mining –Theophano Mitsa, CRC Press 2010
2. Data mining concepts and techniques- Jiawei Han &
Micheline Kamber, Jian Pei, Elsevier (2014)
3. Data mining methods and Techniques: A B M Showkat
Ali, Saleh A Wasimi, Cengage Learning (2009)
4. Introduction to Data mining with case studies: G.K
Gupta PHI (2008)
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