Finding best algorithmic components for clustering microarray data

Vukičević, Milan; Kirchner, Kathrin; Delibašić, Boris; Jovanović, Miloš; Ruhland, Johannes; Suknović, Milija

doi:10.1007/s10115-012-0542-5

Cited by 8 publications

(4 citation statements)

References 54 publications

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“…In recent studies component design method is applied in different fields Delibasic, Vukicevic, Jovanovic & Suknovic, 2013;Vukićević, Kirchner, Delibašić, Jovanović, Ruhland, & Suknović, 2013). In our previous research (Lj.…”

Section: Related Workmentioning

confidence: 99%

Component-Based Decision Trees

Kašćelan

2015

International Journal of Operations Research and Information Systems

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Popular decision tree (DT) algorithms such as ID3, C4.5, CART, CHAID and QUEST may have different results using same data set. They consist of components which have similar functionalities. These components implemented on different ways and they have different performance. The best way to get an optimal DT for a data set is one that use component-based design, which enables user to intelligently select in advance implemented components well suited to specific data set. In this article the authors proposed component-based design of the optimal DT for classification of securities account holders. Research results showed that the optimal algorithm is not one of the original DT algorithms. This fact confirms that the component design provided algorithms with better performance than the original ones. Also, the authors found how the specificities of the data influence the DT components performance. Obtained results of classification can be useful to the future investors in the Montenegrin capital market.

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Section: Related Workmentioning

confidence: 99%

Component-Based Decision Trees

Kašćelan

2015

International Journal of Operations Research and Information Systems

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“…This approach combination of RCs, which originates from different algorithms, can be used to design large number (thousands) of new “hybrid” algorithms. This approach gave very promising results in the area of clustering biomedical (gene expression) data [ 8 , 44 , 45 ].…”

Section: Metalearning Framework For Clustering Biomedical Datamentioning

confidence: 99%

Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

Vukičević

Radovanović

Milovanović

et al. 2014

The Scientific World Journal

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Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.

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“…Clustering algorithms that are automatically looking for the right number of clusters in data tend to detect fewer clusters, so a high number cluster structure is hard to reveal [20]. These algorithms work soundly in finding k clusters in data when k (number of clusters) is much smaller than n (number of objects).…”

Section: Introductionmentioning

confidence: 99%

“…These high-number-clustering structures can be often found in real-life applications (e.g. disease prediction with microarray data [20], clustering of human activity patterns [9]). …”

Section: Introductionmentioning

confidence: 99%

Identifying High-Number-Cluster Structures in RFID Ski Lift Gates Entrance Data

Delibašić

Obradović

2015

Ann. Data. Sci.

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In this paper we identify skier groups in data from RFID ski lift gates entrances. The ski lift gates' entrances are real-life data covering a 5-year period from the largest Serbian skiing resort with a 32,000 skier per hour ski lift capacity. We utilize three representative algorithms from three most widely used clustering algorithm families (representative-based, hierarchical, and density based) and produce 40 algorithm settings for clustering skiing groups. Ski pass sales data was used to validate the produced clustering models. It was assumed that persons who bought ski tickets together are more likely to ski together. AMI and ARI clustering validation measures are reported for each model. In addition, the applicability of the proposed models was evaluated for ski injury prevention. Each clustering model was tested on whether skiing in groups increases risk of injury. Hierarchical clustering algorithms showed to be very efficient in terms of finding the high-number-cluster structure (skiing groups) and for detecting models suitable for injury prevention. Most of the tested clustering algorithms models supported the hypothesis that skiing in groups increases risk of injury.

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Finding best algorithmic components for clustering microarray data

Cited by 8 publications

References 54 publications

Component-Based Decision Trees

Component-Based Decision Trees

Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

Identifying High-Number-Cluster Structures in RFID Ski Lift Gates Entrance Data

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