Data Mining-Concepts and Techniques
Introduction to data mining-Data mining functionalities-Steps
in data mining process- Classification of data mining systems, Major issues in data mining.
Data Wrangling and Preprocessing: Data Preprocessing: An overview-Data cleaning-Data
transformation and Data discretization
General approach to classification-Decision tree induction- Bayes
classification methods- advanced classification methods: Bayesian belief networks-
Classification by Backpropagation- Support Vector Machines-Lazy learners
Types of data in cluster analysis-Partitioning methods- Hierarchical
methods-Advanced cluster analysis: Probabilistic model-based clustering- Clustering high
dimensional data-Outlier analysis
Frequent Pattern Mining: Basic Concepts and a Road Map -
Efficient and scalable frequent item set mining methods: Apriori algorithm, FP-Growth
algorithm- Mining frequent itemsets using vertical data format- Mining closed and max
patterns- Advanced Pattern Mining: Pattern Mining in Multilevel, Multidimensional Space
Other methodologies of data mining: Web
mining-Temporal mining-Spatial mining-Statistical data mining- Visual and audio data
mining- Data mining applications- Data mining and society: Ubiquitous and invisible data
mining- Privacy, Security, and Social Impacts of data mining
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