Compression schemes for mining large datasets: a machine learning perspective
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Datensatz im Suchindex
| _version_ | 1819285031802109952 |
|---|---|
| adam_text | COMPRESSION SCHEMES FOR MINING LARGE DATASETS
/ RAVINDRA BABU, T.
: 2013
TABLE OF CONTENTS / INHALTSVERZEICHNIS
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
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
COMPRESSION SCHEMES FOR MINING LARGE DATASETS
/ RAVINDRA BABU, T.
: 2013
ABSTRACT / INHALTSTEXT
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, COMPRESSING DATA THROUGH NOVEL LOSSY AND NON-LOSSY
SCHEMES, AND CARRYING OUT CLUSTERING AND CLASSIFICATION DIRECTLY IN THE
COMPRESSED DOMAIN. SCHEMES ARE PRESENTED WHICH ARE SHOWN TO BE EFFICIENT
BOTH IN TERMS OF SPACE AND TIME, WHILE SIMULTANEOUSLY PROVIDING THE SAME
OR BETTER CLASSIFICATION ACCURACY, AS ILLUSTRATED USING HIGH-DIMENSIONAL
HANDWRITTEN DIGIT DATA AND A LARGE INTRUSION DETECTION DATASET. TOPICS
AND FEATURES: PRESENTS A CONCISE INTRODUCTION TO DATA MINING
PARADIGMS, DATA COMPRESSION, AND MINING COMPRESSED DATA DESCRIBES A
NON-LOSSY COMPRESSION SCHEME BASED ON RUN-LENGTH ENCODING OF PATTERNS
WITH BINARY VALUED FEATURES PROPOSES A LOSSY COMPRESSION SCHEME THAT
RECOGNIZES A PATTERN AS A SEQUENCE OF FEATURES AND IDENTIFYING
SUBSEQUENCES EXAMINES WHETHER THE IDENTIFICATION OF PROTOTYPES AND
FEATURES CAN BE ACHIEVED SIMULTANEOUSLY THROUGH LOSSY COMPRESSION AND
EFFICIENT CLUSTERING DISCUSSES WAYS TO MAKE USE OF DOMAIN KNOWLEDGE IN
GENERATING ABSTRACTION REVIEWS OPTIMAL PROTOTYPE SELECTION USING GENETIC
ALGORITHMS SUGGESTS POSSIBLE WAYS OF DEALING WITH BIG DATA PROBLEMS
USING MULTIAGENT SYSTEMS A MUST-READ FOR ALL RESEARCHERS INVOLVED IN
DATA MINING AND BIG DATA, THE BOOK PROPOSES EACH ALGORITHM WITHIN A
DISCUSSION OF THE WIDER CONTEXT, IMPLEMENTATION DETAILS AND EXPERIMENTAL
RESULTS. THESE ARE FURTHER SUPPORTED BY BIBLIOGRAPHIC NOTES AND A
GLOSSARY
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| author | Ravindra Babu, T. Narasimha Murty, M. Subrahmanya, S. V. |
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| author_role | aut aut aut |
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| series2 | Advances in computer vision and pattern recognition |
| spellingShingle | Ravindra Babu, T. Narasimha Murty, M. Subrahmanya, S. V. Compression schemes for mining large datasets a machine learning perspective |
| title | Compression schemes for mining large datasets a machine learning perspective |
| title_auth | Compression schemes for mining large datasets a machine learning perspective |
| title_exact_search | Compression schemes for mining large datasets a machine learning perspective |
| title_full | Compression schemes for mining large datasets a machine learning perspective T. Ravindra Babu ; M. Narasimha Murty ; S.V. Subrahmanya |
| title_fullStr | Compression schemes for mining large datasets a machine learning perspective T. Ravindra Babu ; M. Narasimha Murty ; S.V. Subrahmanya |
| title_full_unstemmed | Compression schemes for mining large datasets a machine learning perspective T. Ravindra Babu ; M. Narasimha Murty ; S.V. Subrahmanya |
| title_short | Compression schemes for mining large datasets |
| title_sort | compression schemes for mining large datasets a machine learning perspective |
| title_sub | a machine learning perspective |
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