Semi-supervised learning techniques: k-means clustering in OODB fragmentation

Darabant, Adrian Sergiu; Câmpan, Alina

doi:10.1109/icccyb.2004.1437742

Cited by 9 publications

(8 citation statements)

References 9 publications

(11 reference statements)

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“…A horizontal fragmentation approach that uses data mining clustering methods for partitioning object instances into fragments has been presented in [4], [5], [6], [7]. Essentially, that approach takes full advantage of existing data, where statistics are already present, and develops fragmentation around user applications (queries) that are to be optimized by the obtained fragmentation.…”

Section: Adaptive Horizontal Fragmentation In Object Oriented Databasesmentioning

confidence: 99%

Adaptive Clustering Algorithms

Câmpan

Șerban

2006

Advances in Artificial Intelligence

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Abstract. This paper proposes an adaptive clustering approach. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We have developed adaptive extensions for two traditional clustering algorithms (k-means and Hierarchical Agglomerative Clustering). These extensions can be used for adjusting a clustering, that was established by applying the corresponding non-adaptive clustering algorithm before the feature set changed. We aim to reach the result more efficiently than applying the corresponding non-adaptive algorithm starting from the current clustering or from scratch. Experiments testing the method's efficiency are also reported.

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Section: Adaptive Horizontal Fragmentation In Object Oriented Databasesmentioning

confidence: 99%

Adaptive Clustering Algorithms

Câmpan

Șerban

2006

Advances in Artificial Intelligence

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“…Basically, their solution consists in adapting the well-known algorithm Apriori [5] by selecting the non-overlapping item-sets having highest support and by grouping their respective attributes into one partition. Then, the algorithm exploits a cost model to select an optimal fragmentation schema.Darabant and Campan [33] propose using K -means clustering for efficiently supporting horizontal fragmentation of object-oriented distributed databases. This research has inspired our work.…”

Section: Data-mining-based Fragmentationmentioning

confidence: 99%

“…This research has inspired our work. In more detail, the method proposed in [33] clusters object instances into fragments via taking into account all complex relationships between classes of data objects (aggregation, associations and links induced by complex methods). Finally, Fiolet and Toursel [36] propose a parallel, progressive clustering algorithm to fragment a database and distribute it over a data grid.…”

Section: Data-mining-based Fragmentationmentioning

confidence: 99%

Fragmenting very large XML data warehouses via K-means clustering algorithm

Cuzzocrea

Darmont

Mahboubi

2009

IJBIDM

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XML data sources are gaining popularity in the context of Business Intelligence and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing complex and heterogeneous data. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore, recent research efforts are focusing on horizontal fragmentation techniques, which are able to overcome the above limitations. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses. In this paper, we propose the use of the K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses. We complement our analytical contribution with a comprehensive experimental assessment where we compare the efficiency of our proposal against existing fragmentation algorithms.

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“…A horizontal fragmentation approach that uses data mining clustering methods for partitioning object instances into fragments has been presented in (Darabant and Campan, 2004a), (Darabant and Campan, 2004b), (Darabant and Campan, 2004c), (Darabant et al, 2004). Essentially, that approach takes full advantage of existing data, where statistics are already present, and develops fragmentation around user applications (queries) that are to be optimized by the obtained fragmentation.…”

Section: Adaptive Horizontal Fragmentation In Object Oriented Databasesmentioning

confidence: 99%

Hierarchical Adaptive Clustering

Șerban

Câmpan

2008

Informatica

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This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We propose an adaptive clustering method based on a hierarchical agglomerative approach, Hierarchical Adaptive Clustering (HAC), that adjusts the partitioning into clusters that was established by applying the hierarchical agglomerative clustering algorithm (HACA) (Han and Kamber, 2001) before the feature set changed. We aim to reach the result more efficiently than running HACA again from scratch on the feature-extended object set. Experiments testing the method's efficiency and a practical distributed systems problem in which the HAC method can be efficiently used (the problem of adaptive horizontal fragmentation in object oriented databases) are also reported.

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Semi-supervised learning techniques: k-means clustering in OODB fragmentation

Cited by 9 publications

References 9 publications

Adaptive Clustering Algorithms

Adaptive Clustering Algorithms

Fragmenting very large XML data warehouses via K-means clustering algorithm

Hierarchical Adaptive Clustering

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