Maheswari et al., 2021 - Google Patents
Kernelized Spectral Clustering based Conditional MapReduce function with big dataMaheswari et al., 2021
View PDF- Document ID
- 14835398363840492743
- Author
- Maheswari K
- Ramakrishnan M
- Publication year
- Publication venue
- International Journal of Computers and Applications
External Links
Snippet
Clustering is the significant data mining technique for big data analysis, where large volume data are grouped. The resulting of clustering is to minimize the dimensionality while accessing large volume of data. The several data mining techniques have been developed …
- 230000003595 spectral 0 title abstract description 41
Classifications
-
- 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
- G06F17/30—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
- 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
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- 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
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- 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
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- 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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tang et al. | Explainable multi-task learning for multi-modality biological data analysis | |
| He et al. | SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines | |
| Mushtaq et al. | Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets | |
| Fong et al. | Accelerated PSO swarm search feature selection for data stream mining big data | |
| Nguyen et al. | SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra | |
| van Stein et al. | Optimally weighted cluster kriging for big data regression | |
| Xu et al. | A feasible density peaks clustering algorithm with a merging strategy | |
| Kianmehr et al. | Effectiveness of support vector machine for crime hot-spots prediction | |
| Al-Batah et al. | Enhancement over DBSCAN satellite spatial data clustering | |
| CN116109121B (en) | User demand mining method and system based on big data analysis | |
| Messenger et al. | Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population | |
| Birzhandi et al. | Reduction of training data for support vector machine: a survey | |
| Tripathy et al. | AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis: G. Tripathy, A. Sharaff | |
| EP3832491A1 (en) | Methods for processing a plurality of candidate annotations of a given instance of an image, and for learning parameters of a computational model | |
| Zhang et al. | Out-of-sample data visualization using bi-kernel t-SNE | |
| Modak | A new nonparametric interpoint distance-based measure for assessment of clustering | |
| Rodrigues et al. | High-fidelity reproduction of central galaxy joint distributions with neural networks | |
| Hofmeyr | Improving spectral clustering using the asymptotic value of the normalized cut | |
| Maheswari et al. | Kernelized Spectral Clustering based Conditional MapReduce function with big data | |
| Hess et al. | Object detection as probabilistic set prediction | |
| Du et al. | An improved density peaks clustering algorithm by automatic determination of cluster centres | |
| Teixeira et al. | Contrastive dissimilarity: optimizing performance on imbalanced and limited data sets | |
| Beavers et al. | Data nuggets: A method for reducing big data while preserving data structure | |
| Dayan et al. | Expressivity of geometric inhomogeneous random graphs—metric and non-metric | |
| Granata et al. | Network distances for weighted digraphs |