Barger et al., 2016 - Google Patents
k-means for streaming and distributed big sparse dataBarger et al., 2016
View PDF- Document ID
- 6127933813352670687
- Author
- Barger A
- Feldman D
- Publication year
- Publication venue
- Proceedings of the 2016 SIAM International Conference on Data Mining
External Links
Snippet
We provide the first streaming algorithm for computing a provable approximation to the k- means of sparse Big Data. Here, sparse Big Data is a stream of n vectors in ℝ d, where each vector has O (1) non-zeroes entries and possibly d≥ n. Eg, adjacency matrix of a graph …
- 239000011159 matrix material 0 abstract description 4
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30477—Query execution
- G06F17/30483—Query execution of query operations
- G06F17/30486—Unary operations; data partitioning operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30312—Storage and indexing structures; Management thereof
- G06F17/30321—Indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30289—Database design, administration or maintenance
- G06F17/30303—Improving data quality; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
- G06F17/30946—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30067—File systems; File servers
- G06F17/30129—Details of further file system functionalities
- G06F17/3015—Redundancy elimination performed by the file system
- G06F17/30156—De-duplication implemented within the file system, e.g. based on file segments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10013477B2 (en) | Accelerated discrete distribution clustering under wasserstein distance | |
Nishimura et al. | Restreaming graph partitioning: simple versatile algorithms for advanced balancing | |
Ravi et al. | Large scale distributed semi-supervised learning using streaming approximation | |
Wang et al. | Clustering aggregation by probability accumulation | |
CN104156463A (en) | Big-data clustering ensemble method based on MapReduce | |
Xu et al. | Harmonious Hashing. | |
Fries et al. | Phidj: Parallel similarity self-join for high-dimensional vector data with mapreduce | |
WO2015001416A1 (en) | Multi-dimensional data clustering | |
Dhulipala et al. | Hierarchical agglomerative graph clustering in nearly-linear time | |
Imre et al. | Spectrum-preserving sparsification for visualization of big graphs | |
Barger et al. | k-means for streaming and distributed big sparse data | |
CN115311483A (en) | Incomplete multi-view clustering method and system based on local structure and balance perception | |
Duan et al. | Distributed in-memory vocabulary tree for real-time retrieval of big data images | |
Li et al. | Hilbert curve projection distance for distribution comparison | |
Chang et al. | GraphCS: Graph-based client selection for heterogeneity in federated learning | |
US20200104425A1 (en) | Techniques for lossless and lossy large-scale graph summarization | |
Moertini et al. | Enhancing parallel k-means using map reduce for discovering knowledge from big data | |
Chen et al. | DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark | |
Amagata et al. | Identifying the most interactive object in spatial databases | |
Jiang et al. | A survey of real-time approximate nearest neighbor query over streaming data for fog computing | |
CN105354243B (en) | The frequent probability subgraph search method of parallelization based on merger cluster | |
Burdescu et al. | A Spatial Segmentation Method. | |
Wu et al. | Research and improve on K-means algorithm based on hadoop | |
Penschuck et al. | Recent advances in scalable network generation 1 | |
Dai et al. | FlexGM: An Adaptive Runtime System to Accelerate Graph Matching Networks on GPUs |