MY152935A - Method and apparatus for adaptive data clustering - Google Patents
Method and apparatus for adaptive data clusteringInfo
- Publication number
- MY152935A MY152935A MYPI2010002979A MY152935A MY 152935 A MY152935 A MY 152935A MY PI2010002979 A MYPI2010002979 A MY PI2010002979A MY 152935 A MY152935 A MY 152935A
- Authority
- MY
- Malaysia
- Prior art keywords
- principle axis
- data points
- adaptive data
- data clustering
- cluster centers
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2137—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
- G06F18/21375—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps involving differential geometry, e.g. embedding of pattern manifold
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Image Analysis (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
[0051] THE PRESENT INVENTION PROVIDES A METHOD OF CLUSTERING DATA POINTS IN A MULTI-DIMENSIONAL FEATURE SPACE. THE METHOD COMPRISES FORMING (301) A PRINCIPLE AXIS BENDING GAUSSIAN MODEL TO GROUP THE DATA POINTS ALONG A CURVED PRINCIPLE AXIS ON A ORTHOGONAL PLANE; CARRYING OUT (302) A FUZZY C-MEANS FOR LOCATING CLUSTER CENTERS OF THE PRINCIPLE AXIS BENDING GAUSSIAN MODEL; AND PERFORMING (303) ITERATIVELY EXPECTATION-MAXIMIZATION (EM) ALGORITHM TO UPDATE AND OPTIMIZE THE CLUSTER CENTERS. [0052]
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
MYPI2010002979 MY152935A (en) | 2010-06-22 | 2010-06-22 | Method and apparatus for adaptive data clustering |
PCT/MY2010/000238 WO2011162589A1 (en) | 2010-06-22 | 2010-10-29 | Method and apparatus for adaptive data clustering |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
MYPI2010002979 MY152935A (en) | 2010-06-22 | 2010-06-22 | Method and apparatus for adaptive data clustering |
Publications (1)
Publication Number | Publication Date |
---|---|
MY152935A true MY152935A (en) | 2014-12-15 |
Family
ID=45371615
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
MYPI2010002979 MY152935A (en) | 2010-06-22 | 2010-06-22 | Method and apparatus for adaptive data clustering |
Country Status (2)
Country | Link |
---|---|
MY (1) | MY152935A (en) |
WO (1) | WO2011162589A1 (en) |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104809098A (en) * | 2014-01-27 | 2015-07-29 | 华为技术有限公司 | Method and device for determining statistical model parameter based on expectation-maximization algorithm |
CN105913074B (en) * | 2016-04-05 | 2019-01-11 | 西安电子科技大学 | Based on amplitude and the united SAR image moving-target clustering method of radial velocity |
CN106408569B (en) * | 2016-08-29 | 2018-12-04 | 北京航空航天大学 | Based on the brain MRI image dividing method for improving Fuzzy C-Means Cluster Algorithm |
US10451544B2 (en) | 2016-10-11 | 2019-10-22 | Genotox Laboratories | Methods of characterizing a urine sample |
WO2018208970A1 (en) | 2017-05-09 | 2018-11-15 | Massachusetts Eye And Ear Infirmary | Biomarkers for age-related macular degeneration |
CN108828583B (en) * | 2018-06-15 | 2022-06-28 | 西安电子科技大学 | Point trace clustering method based on fuzzy C mean value |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020129038A1 (en) * | 2000-12-18 | 2002-09-12 | Cunningham Scott Woodroofe | Gaussian mixture models in a data mining system |
US20030154181A1 (en) * | 2002-01-25 | 2003-08-14 | Nec Usa, Inc. | Document clustering with cluster refinement and model selection capabilities |
US7764819B2 (en) * | 2006-01-25 | 2010-07-27 | Siemens Medical Solutions Usa, Inc. | System and method for local pulmonary structure classification for computer-aided nodule detection |
-
2010
- 2010-06-22 MY MYPI2010002979 patent/MY152935A/en unknown
- 2010-10-29 WO PCT/MY2010/000238 patent/WO2011162589A1/en active Application Filing
Also Published As
Publication number | Publication date |
---|---|
WO2011162589A1 (en) | 2011-12-29 |
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