@InProceedings{8553146,
  author = {S. D. Xenaki and K. D. Koutroumbas and A. A. Rontogiannis},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {A Novel Online Generalized Possibilistic Clustering Algorithm for Big Data Processing},
  year = {2018},
  pages = {2628-2632},
  abstract = {In this paper a novel efficient online possibilistic c-means clustering algorithm, called Online Generalized Adaptive Possibilistic C-Means (O-GAPCM), is presented. The algorithm extends the abilities of the Adaptive Possibilistic C-Means (APCM) algorithm, allowing the study of cases where the data form compact and hyper-ellipsoidally shaped clusters in the feature space. In addition, the algorithm performs online processing, that is the data vectors are processed one-by-one and their impact is memorized to suitably defined parameters. It also embodies new procedures for creating new clusters and merging existing ones. Thus, O-GAPCM is able to unravel on its own the number and the actual hyper-ellipsoidal shape of the physical clusters formed by the data. Experimental results verify the effectiveness of O-GAPCM both in terms of accuracy and time efficiency.},
  keywords = {Big Data;pattern clustering;possibility theory;vectors;O-GAPCM;physical clusters;big data processing;data vectors;online generalized adaptive possibilistic c-means;online generalized possibilistic clustering;APCM algorithm;hyper-ellipsoidally shaped clusters;feature space;Clustering algorithms;Signal processing algorithms;Phase change materials;Europe;Shape;Signal processing;Merging;possibilistic clustering;online clustering;parameter adaptivity;hyperspectral imaging},
  doi = {10.23919/EUSIPCO.2018.8553146},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439026.pdf},
}

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The algorithm extends the abilities of the Adaptive Possibilistic C-Means (APCM) algorithm, allowing the study of cases where the data form compact and hyper-ellipsoidally shaped clusters in the feature space. In addition, the algorithm performs online processing, that is the data vectors are processed one-by-one and their impact is memorized to suitably defined parameters. It also embodies new procedures for creating new clusters and merging existing ones. Thus, O-GAPCM is able to unravel on its own the number and the actual hyper-ellipsoidal shape of the physical clusters formed by the data. Experimental results verify the effectiveness of O-GAPCM both in terms of accuracy and time efficiency.","keywords":"Big Data;pattern clustering;possibility theory;vectors;O-GAPCM;physical clusters;big data processing;data vectors;online generalized adaptive possibilistic c-means;online generalized possibilistic clustering;APCM algorithm;hyper-ellipsoidally shaped clusters;feature space;Clustering algorithms;Signal processing algorithms;Phase change materials;Europe;Shape;Signal processing;Merging;possibilistic clustering;online clustering;parameter adaptivity;hyperspectral imaging","doi":"10.23919/EUSIPCO.2018.8553146","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439026.pdf","bibtex":"@InProceedings{8553146,\n author = {S. D. Xenaki and K. D. Koutroumbas and A. A. Rontogiannis},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {A Novel Online Generalized Possibilistic Clustering Algorithm for Big Data Processing},\n year = {2018},\n pages = {2628-2632},\n abstract = {In this paper a novel efficient online possibilistic c-means clustering algorithm, called Online Generalized Adaptive Possibilistic C-Means (O-GAPCM), is presented. The algorithm extends the abilities of the Adaptive Possibilistic C-Means (APCM) algorithm, allowing the study of cases where the data form compact and hyper-ellipsoidally shaped clusters in the feature space. In addition, the algorithm performs online processing, that is the data vectors are processed one-by-one and their impact is memorized to suitably defined parameters. It also embodies new procedures for creating new clusters and merging existing ones. 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