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Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design

Book Description

Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design presents innovative, cutting-edge fuzzy techniques that highlight the relevance of fuzziness for huge data sets in the perspective of scalability issues, from both a theoretical and experimental point of view. It covers a wide scope of research areas including data representation, structuring and querying as well as information retrieval and data mining. It encompasses different forms of databases, including data warehouses, data cubes, tabular or relational data, and many applications among which music warehouses, video mining, bioinformatics, semantic web and data streams.

Table of Contents

  1. Copyright
  2. List of Reviewers
  3. Foreword
  4. Preface
  5. Acknowledgment
  6. Introductory Chapters
    1. Electronic Hardware for Fuzzy Computation
      1. ABSTRACT
      2. INTRODUCTION
      3. HARDWARE IMPLEMENTATION OF FUZZY INFERENCE SYSTEMS
      4. SCALABILITY AND NEW TRENDS IN FUZZY HARDWARE
      5. HARDWARE FOR FUZZY DATA-MINING
      6. CONCLUDING REMARKS
    2. REFERENCES
      1. ENDNOTE
    3. Scaling Fuzzy Models
      1. ABSTRACT
      2. INTRODUCTION
      3. SCALING UNSUPERVISED FUZZY LEARNING
      4. ACKNOWLEDGMENT
    4. REFERENCES
  7. Databases and Queries
    1. Using Fuzzy Song Sets in Music Warehouses
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. QUERY SCENARIO
      5. AN ALGEBRA FOR FUZZY SONG SETS
      6. THE MUSIC WAREHOUSE CUBES
      7. STORAGE OPTIONS
      8. FZSET FUNCTIONS AND OPERATORS
      9. GENERALIZATION TO OTHER DOMAINS
      10. CONCLUSION AND FUTURE WORK
      11. ACKNOWLEDGMENT
    2. REFERENCES
      1. ENDNOTE
    3. Mining Association Rules from Fuzzy DataCubes
      1. ABSTRACT
      2. INTRODUCTION
      3. ASSOCIATION RULE EXTRACTION
      4. THE FUZZY MULTIDIMENSIONAL MODEL
      5. COMPLEXITY MEASURE
      6. QUALITY MEASURES
      7. COGARE ALGORITHM
      8. EXPERIMENTS
      9. CONCLUSION
    4. REFERENCES
      1. ENDNOTES
    5. Scalable Reasoning with Tractable Fuzzy Ontology Languages
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SCALABLE QUERY ANSWERING WITH FUZZY DL-LITE
      5. SCALABLE KNOWLEDGE CLASSIFICATION WITH FUZZY EL+
      6. DISCUSSION AND FUTURE WORK
    6. REFERENCES
      1. ENDNOTES
    7. A Random Set and Prototype Theory Model of Linguistic Query Evaluation
      1. ABSTRACT
      2. INTRODUCTION
      3. LABEL SEMANTICS
      4. A PROTOTYPE THEORY INTERPRETATION OF LABEL SEMANTICS
      5. QUANTIFIED STATEMENTS AND QUERY EVALUATION
      6. A SCALEABLE ALGORITHM FOR EVALUATING SIMPLE LINGUISTIC QUERIES
      7. CONCLUSION
      8. ACKNOWLEDGMENT
    8. REFERENCES
      1. ENDNOTE
    9. A Flexible Language for Exploring Clustered Search Results
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND OF THE PROPOSED LANGUAGE
      4. PRACTICAL UTILITY OF THE EXPLORATORY LANGUAGE: A USE CASE EXAMPLE
      5. THE FLEXIBLE EXPLORATORY LANGUAGE
      6. SCALABILITY ISSUES
      7. CONCLUSION
      8. ACKNOWLEDGMENT
    10. REFERENCES
  8. Summarization
    1. Linguistic Data Summarization: A High Scalability through the Use of Natural Language?
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. LINGUISTIC DATA(BASE) SUMMARIES
      5. PRACTICAL DETERMINATION OF LINGUISTIC DATA SUMMARIES
      6. SOME FUTURE RESEARCH DIRECTIONS
      7. CONCLUDING REMARKS
    2. REFERENCES
      1. ADDITIONAL READING
    3. Human Focused Summarizing Statistics Using OWA Operators
      1. ABSTRACT
      2. INTRODUCTION
      3. OWA OPERATORS
      4. MODELING BASIC STATISTICS WITH OWA OPERATORS
      5. A CLASS OF LINEAR STATISTICS
      6. A GENERAL OWA APPROACH TO SUMMARIZING STATISTICS
      7. WEIGHTED SUMMARIZING STATISTICS
      8. MODELING THE MODE
      9. CONCLUSION
    4. REFERENCES
    5. (Approximate) Frequent Item Set Mining Made Simple with a Split and Merge Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. FREQUENT ITEM SET MINING
      4. A SIMPLE SPLIT AND MERGE ALGORITHM
      5. EXACT FREQUENT ITEM SET MINING EXPERIMENTS
      6. APPROXIMATE FREQUENT ITEM SET MINING
      7. UNLIMITED ITEM INSERTIONS
      8. LIMITED ITEM INSERTIONS
      9. APPROXIMATE FREQUENT ITEM SET MINING EXPERIMENTS
      10. CONCLUSION
      11. SOFTWARE
    6. REFERENCES
      1. ENDNOTES
    7. Fuzzy Association Rules to Summarise Multiple Taxonomies in Large Databases
      1. ABSTRACT
      2. INTRODUCTION
      3. FUZZY SETS IN INFORMATION SYSTEMS
      4. EXTENDING ASSOCIATION RULES TO FUZZY CATEGORIES
      5. ALTERNATIVE INTERPRETATION OF RELATIONS AS MASS ASSIGNMENTS
      6. CLOSED WORLD MASS-BASED ASSOCIATION RULES
      7. FAST CALCULATION OF FUZZY CONFIDENCE INTERVAL
      8. EXPERIMENTS
      9. SUMMARY
      10. ACKNOWLEDGMENT
    8. REFERENCES
    9. Fuzzy Cluster Analysis of Larger Data Sets
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. FUZZY C-MEANS ALGORITHM (FCM)
      5. ALTERNATIVE FUZZIFIER FUNCTION
      6. NEIGHBOURHOOD REPRESENTATION OF DATA
      7. FUZZY CLUSTERING USING NEIGHBOURHOOD INFORMATION
      8. EXPERIMENTAL RESULTS
      9. CONCLUSION
      10. ACKNOWLEDGMENT
    10. REFERENCES
    11. Fuzzy Clustering with Repulsive Prototypes
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FUZZY CLUSTERING
      5. REPULSIVE GUSTAFSON-KESSEL CLUSTERING
      6. EXPERIMENTAL RESULTS
      7. CONCLUSION
    12. REFERENCES
  9. Real-World Challenges
    1. Early Warning from Car Warranty Data using a Fuzzy Logic Technique
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FUZZY-BASED MONITORING OF WARRANTY DATA
      5. FROM FUZZY SHIFTS TO FUZZY TRENDS
      6. CASE STUDY
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. ACKNOWLEDGMENT
    2. REFERENCES
    3. High Scale Fuzzy Video Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. FROM VIDEO TO TRAINING SETS
      4. LEARNING AND DETECTING HIGH LEVEL CONCEPTS
      5. NUMBER OF DECISION TREES FOR HIGH SCALE MINING
      6. CONCLUSION
    4. REFERENCES
      1. ENDNOTE
    5. Fuzzy Clustering of Large Relational Bioinformatics Datasets
      1. ABSTRACT
      2. INTRODUCTION
      3. EXTENDED NON-EUCLIDEAN RELATIONAL FUZZY C-MEANS (ENERF)
      4. EXTENDED CORRELATION CLUSTER VALIDITY, ECCV
      5. REFSEQ GENE PRODUCT DATASET
      6. ENERF EXPERIMENTS ON THE REFSEQ DATASET
      7. CONCLUSION
    6. REFERENCES
  10. Compilation of References
  11. About the Contributors
  12. Index