Reusable components for partitioning clustering algorithms

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 subproblems 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.

Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

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A method for design of data-tailored partitioning algorithms for optimizing the number of clusters in microarray analysis

“…These are well-documented, frequently occurring solutions for specific sub-problems in a family of algorithms [6] (in this case representative based clustering). RCs have the same I/O structure as the sub-problems (i.e.…”

“…Although there are some recommendations regarding algorithm selection for clustering biological data [5], there is no consensus about the best algorithm for such a hard clustering task. In this regard, the reusable-component (RC) based approach ( [6] for clustering algorithms could give a clearer direction.…”

Internal Evaluation Measures as Proxies for External Indices in Clustering Gene Expression Data

“…In this paper we use the framework for component based design of representative based clustering algorithms [6] to construct a large class of representative-based clustering algorithms. Clustering algorithms used in our study are designed by varying RCs from the following four sub-problems: initializing representatives, measuring distance, updating representatives and evaluating clusters.…”

“…As a step towards the solution for this problem, we propose a method to detect internal measures that are highly correlated to selected external indices, and we suggest a way to utilize these internal measures in representative based clustering algorithms. Both detection and utilization steps are achieved by using a component-based clustering algorithm design [6] together with recently recommended external measure for evaluation of clustering algorithms [7].…”