Customer purchase data analysis and clustering high-dimensional data
by Bater Makhabel
Learning Data Mining with R
- Learning Data Mining with R
- Table of Contents
- Learning Data Mining with R
- Credits
- About the Author
- Acknowledgments
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Warming Up
- 2. Mining Frequent Patterns, Associations, and Correlations
- An overview of associations and patterns
- Market basket analysis
- Hybrid association rules mining
- Mining sequence dataset
- The R implementation
- High-performance algorithms
- Time for action
- Summary
- 3. Classification
- Classification
- Generic decision tree induction
- High-value credit card customers classification using ID3
- Web spam detection using C4.5
- Web key resource page judgment using CART
- Trojan traffic identification method and Bayes classification
- Identify spam e-mail and Naïve Bayes classification
- Rule-based classification of player types in computer games and rule-based classification
- Time for action
- Summary
- 4. Advanced Classification
- 5. Cluster Analysis
- 6. Advanced Cluster Analysis
- Customer categorization analysis of e-commerce and DBSCAN
- Clustering web pages and OPTICS
- Visitor analysis in the browser cache and DENCLUE
- Recommendation system and STING
- Web sentiment analysis and CLIQUE
- Opinion mining and WAVE clustering
- User search intent and the EM algorithm
- Customer purchase data analysis and clustering high-dimensional data
- SNS and clustering graph and network data
- Time for action
- Summary
- 7. Outlier Detection
- Credit card fraud detection and statistical methods
- Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
- Intrusion detection and density-based methods
- Intrusion detection and clustering-based methods
- Monitoring the performance of the web server and classification-based methods
- Detecting novelty in text, topic detection, and mining contextual outliers
- Collective outliers on spatial data
- Outlier detection in high-dimensional data
- Time for action
- Summary
- 8. Mining Stream, Time-series, and Sequence Data
- 9. Graph Mining and Network Analysis
- 10. Mining Text and Web Data
- A. Algorithms and Data Structures
- Index
For high-dimensional data-space clustering, two major issues occur: efficiency and quality. New algorithms are needed to deal with this type of dataset. Two popular strategies are applied to it. One is the subspace-clustering strategy to find the cluster in the subspace of the original dataset space. Another is the dimensionality-reduction strategy, which is a lower dimensional data space created for further clustering.
MAFIA is an efficient and scalable subspace-clustering algorithm for high-dimensional and large datasets.
The summarized pseudocode for the MAFIA algorithm is as follows:
The summarized pseudocode for the parallel MAFIA algorithm is as follows:
The summarized pseudocodes for the SURFING algorithm are as follows. It selects interesting features from the original attributes of the dataset.
-
No Comment