Reusable components for partitioning clustering algorithms
Само за регистроване кориснике
2009
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Clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these solutions occur in more than one clustering algorithm. Mostly, new clustering algorithms include frequently occurring solutions to typical sub-problems from clustering, as well as from other machine-learning algorithms. The problem is that these solutions are usually integrated in their algorithms, and that original algorithms are not designed to share solutions to sub-problems outside the original algorithm easily. We propose a way of designing cluster algorithms and to improve existing ones, based on reusable components. Reusable components are well-documented, frequently occurring solutions to specific sub-problems in a specific area.... Thus we identify reusable components, first, as solutions to characteristic sub-problems in partitioning cluster algorithms, and, further, identify a generic structure for the design of partitioning cluster algorithms. We analyze some partitioning algorithms (K-means, X-means, MPCK-means, and Kohonen SOM), and identify reusable components in them. We give examples of how new cluster algorithms can be designed based on them.
Кључне речи:
X-means / Reusable component / Partitioning clustering / MPCK-means / Kohonen SOM / K-means / Generic / Cluster algorithmИзвор:
Artificial Intelligence Review, 2009, 32, 1-4, 59-75Издавач:
- Springer, Dordrecht
Финансирање / пројекти:
- Project: 12013
DOI: 10.1007/s10462-009-9133-6
ISSN: 0269-2821
WoS: 000272847700004
Scopus: 2-s2.0-75149150690
Институција/група
Fakultet organizacionih naukaTY - JOUR AU - Delibašić, Boris AU - Kirchner, Kathrin AU - Ruhland, Johannes AU - Jovanović, Miloš AU - Vukićević, Milan PY - 2009 UR - https://rfos.fon.bg.ac.rs/handle/123456789/546 AB - Clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these solutions occur in more than one clustering algorithm. Mostly, new clustering algorithms include frequently occurring solutions to typical sub-problems from clustering, as well as from other machine-learning algorithms. The problem is that these solutions are usually integrated in their algorithms, and that original algorithms are not designed to share solutions to sub-problems outside the original algorithm easily. We propose a way of designing cluster algorithms and to improve existing ones, based on reusable components. Reusable components are well-documented, frequently occurring solutions to specific sub-problems in a specific area. Thus we identify reusable components, first, as solutions to characteristic sub-problems in partitioning cluster algorithms, and, further, identify a generic structure for the design of partitioning cluster algorithms. We analyze some partitioning algorithms (K-means, X-means, MPCK-means, and Kohonen SOM), and identify reusable components in them. We give examples of how new cluster algorithms can be designed based on them. PB - Springer, Dordrecht T2 - Artificial Intelligence Review T1 - Reusable components for partitioning clustering algorithms EP - 75 IS - 1-4 SP - 59 VL - 32 DO - 10.1007/s10462-009-9133-6 UR - conv_1226 ER -
@article{ author = "Delibašić, Boris and Kirchner, Kathrin and Ruhland, Johannes and Jovanović, Miloš and Vukićević, Milan", year = "2009", abstract = "Clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these solutions occur in more than one clustering algorithm. Mostly, new clustering algorithms include frequently occurring solutions to typical sub-problems from clustering, as well as from other machine-learning algorithms. The problem is that these solutions are usually integrated in their algorithms, and that original algorithms are not designed to share solutions to sub-problems outside the original algorithm easily. We propose a way of designing cluster algorithms and to improve existing ones, based on reusable components. Reusable components are well-documented, frequently occurring solutions to specific sub-problems in a specific area. Thus we identify reusable components, first, as solutions to characteristic sub-problems in partitioning cluster algorithms, and, further, identify a generic structure for the design of partitioning cluster algorithms. We analyze some partitioning algorithms (K-means, X-means, MPCK-means, and Kohonen SOM), and identify reusable components in them. We give examples of how new cluster algorithms can be designed based on them.", publisher = "Springer, Dordrecht", journal = "Artificial Intelligence Review", title = "Reusable components for partitioning clustering algorithms", pages = "75-59", number = "1-4", volume = "32", doi = "10.1007/s10462-009-9133-6", url = "conv_1226" }
Delibašić, B., Kirchner, K., Ruhland, J., Jovanović, M.,& Vukićević, M.. (2009). Reusable components for partitioning clustering algorithms. in Artificial Intelligence Review Springer, Dordrecht., 32(1-4), 59-75. https://doi.org/10.1007/s10462-009-9133-6 conv_1226
Delibašić B, Kirchner K, Ruhland J, Jovanović M, Vukićević M. Reusable components for partitioning clustering algorithms. in Artificial Intelligence Review. 2009;32(1-4):59-75. doi:10.1007/s10462-009-9133-6 conv_1226 .
Delibašić, Boris, Kirchner, Kathrin, Ruhland, Johannes, Jovanović, Miloš, Vukićević, Milan, "Reusable components for partitioning clustering algorithms" in Artificial Intelligence Review, 32, no. 1-4 (2009):59-75, https://doi.org/10.1007/s10462-009-9133-6 ., conv_1226 .