Compression Schemes for Mining Large Datasets A Machine Learning Perspective /

• Κύριοι συγγραφείς: Ravindra Babu, T. (Συγγραφέας), Narasimha Murty, M. (Συγγραφέας), Subrahmanya, S.V (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: London : Springer London : Imprint: Springer, 2013.
• Σειρά: Advances in Computer Vision and Pattern Recognition,
• Θέματα: Computer science.; Data mining.; Artificial intelligence.; Pattern recognition.; Computer Science.; Pattern Recognition.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).

As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times. This unique text/reference addresses the challenges of data abstraction generation using a least number of database scans, co...

Πίνακας περιεχομένων:

• Introduction
• Data Mining Paradigms
• Run-Length Encoded Compression Scheme
• Dimensionality Reduction by Subsequence Pruning
• Data Compaction through Simultaneous Selection of Prototypes and Features
• Domain Knowledge-Based Compaction
• Optimal Dimensionality Reduction
• Big Data Abstraction through Multiagent Systems
• Intrusion Detection Dataset: Binary Representation.