Hybrid data labeling algorithm for clustering large mixed type data
Abstract Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a...
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Gespeichert in:
| Autor*in: |
Sangam, Ravi Sankar [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent information systems - Springer US, 1992, 45(2014), 2 vom: 14. Dez., Seite 273-293 |
|---|---|
| Übergeordnetes Werk: |
volume:45 ; year:2014 ; number:2 ; day:14 ; month:12 ; pages:273-293 |
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10.1007/s10844-014-0348-x |
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| 520 | |a Abstract Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to partitioning the data points in that sample into clusters. In this approach, the data points, that are not sampled, do not get their cluster labels. The process of allocating unlabeled data points into proper clusters has been well explored purely in numerical or categorical domain only, but not the both. In this paper, we propose a hybrid similarity coefficient to find the resemblance between an unlabeled data point and a cluster, based on the importance of categorical attribute values and the mean values of numerical attributes. Furthermore, we propose a Hybrid Data Labeling Algorithm (HDLA), based on this similarity coefficient to designate an appropriate cluster label to each unlabeled data point. We analyze its time complexity and perform various experiments using synthetic and real world datasets to demonstrate the efficacy of HDLA. | ||
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Abstract Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to partitioning the data points in that sample into clusters. In this approach, the data points, that are not sampled, do not get their cluster labels. The process of allocating unlabeled data points into proper clusters has been well explored purely in numerical or categorical domain only, but not the both. In this paper, we propose a hybrid similarity coefficient to find the resemblance between an unlabeled data point and a cluster, based on the importance of categorical attribute values and the mean values of numerical attributes. Furthermore, we propose a Hybrid Data Labeling Algorithm (HDLA), based on this similarity coefficient to designate an appropriate cluster label to each unlabeled data point. We analyze its time complexity and perform various experiments using synthetic and real world datasets to demonstrate the efficacy of HDLA. © Springer Science+Business Media New York 2014 |
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Abstract Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to partitioning the data points in that sample into clusters. In this approach, the data points, that are not sampled, do not get their cluster labels. The process of allocating unlabeled data points into proper clusters has been well explored purely in numerical or categorical domain only, but not the both. In this paper, we propose a hybrid similarity coefficient to find the resemblance between an unlabeled data point and a cluster, based on the importance of categorical attribute values and the mean values of numerical attributes. Furthermore, we propose a Hybrid Data Labeling Algorithm (HDLA), based on this similarity coefficient to designate an appropriate cluster label to each unlabeled data point. We analyze its time complexity and perform various experiments using synthetic and real world datasets to demonstrate the efficacy of HDLA. © Springer Science+Business Media New York 2014 |
| abstract_unstemmed |
Abstract Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to partitioning the data points in that sample into clusters. In this approach, the data points, that are not sampled, do not get their cluster labels. The process of allocating unlabeled data points into proper clusters has been well explored purely in numerical or categorical domain only, but not the both. In this paper, we propose a hybrid similarity coefficient to find the resemblance between an unlabeled data point and a cluster, based on the importance of categorical attribute values and the mean values of numerical attributes. Furthermore, we propose a Hybrid Data Labeling Algorithm (HDLA), based on this similarity coefficient to designate an appropriate cluster label to each unlabeled data point. We analyze its time complexity and perform various experiments using synthetic and real world datasets to demonstrate the efficacy of HDLA. © Springer Science+Business Media New York 2014 |
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| title_short |
Hybrid data labeling algorithm for clustering large mixed type data |
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https://doi.org/10.1007/s10844-014-0348-x |
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Om, Hari |
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