[go: up one dir, main page]

He et al., 2014 - Google Patents

Parallel feature selection using positive approximation based on mapreduce

He et al., 2014

Document ID
8900254758403170805
Author
He Q
Cheng X
Zhuang F
Shi Z
Publication year
Publication venue
2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

External Links

Snippet

Over the last few decades, feature selection has been a hot research area in pattern recognition and machine learning, and many famous feature selection algorithms have been proposed. Among them, feature selection using positive approximation (FSPA) is an …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30424Query processing
    • G06F17/30533Other types of queries
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • G06F17/30595Relational databases
    • G06F17/30598Clustering or classification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30312Storage and indexing structures; Management thereof
    • G06F17/30321Indexing structures
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705Clustering or classification
    • G06F17/3071Clustering or classification including class or cluster creation or modification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061Partitioning or combining of resources
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30289Database design, administration or maintenance
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30067File systems; File servers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6218Clustering techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/16Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
He et al. Mr-dbscan: an efficient parallel density-based clustering algorithm using mapreduce
Cheng et al. Fast algorithms for maximal clique enumeration with limited memory
Xiao et al. SMK-means: an improved mini batch k-means algorithm based on mapreduce with big data
Lin et al. Mining high utility itemsets in big data
Gupta et al. Scalable machine‐learning algorithms for big data analytics: a comprehensive review
Anchalia et al. The k-nearest neighbor algorithm using MapReduce paradigm
Nasridinov et al. Decision tree construction on GPU: ubiquitous parallel computing approach
CN104156463A (en) Big-data clustering ensemble method based on MapReduce
Xu et al. Distributed maximal clique computation
Xu et al. Distributed maximal clique computation and management
Mayer et al. Streamlearner: Distributed incremental machine learning on event streams: Grand challenge
He et al. Parallel feature selection using positive approximation based on mapreduce
Zhou et al. An effective ensemble pruning algorithm based on frequent patterns
Xiao et al. A survey of parallel clustering algorithms based on spark
Chen et al. Clustering in big data
Xu Research and implementation of improved random forest algorithm based on Spark
He et al. Parallel outlier detection using kd-tree based on mapreduce
Xu et al. New approach to eliminate structural redundancy in case resource pools using α mutual information
Bawane et al. Clustering algorithms in MapReduce: a review
Liu et al. Multiple submodels parallel support vector machine on spark
Doulkeridis et al. Parallel and distributed processing of spatial preference queries using keywords
Zhang et al. Self‐Adaptive K‐Means Based on a Covering Algorithm
Agrawal et al. High performance big data clustering
Jia et al. An improved FP-growth algorithm based on SOM partition
Zhao et al. A grid-based chameleon algorithm based on the tissue-like P system with promoters and inhibitors