clustering in data mining is a supreme step toward organizing data into some meaningful patterns. It plays an extremely crucial role in the entire KDD process, and also as categorizing data is one of the most rudiment...
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clustering data streams is a challenging problem in mining data streams. Data streams need to be read by a clustering algorithm in a single pass with limited time, and memory whereas they may change over time. Differe...
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Designing an experiment for programming education research, in which collecting and interpreting a large number of qualitative data about programmers is required, needs careful consideration in order to validate the e...
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ISBN:
(纸本)9781450314640
Designing an experiment for programming education research, in which collecting and interpreting a large number of qualitative data about programmers is required, needs careful consideration in order to validate the experiment. When it comes to finding a pattern in the programming behaviour of a specific group of programmers (e,g. novice, intermediate or expert programmers), one of the critical issues is the selection of similar participants who can be placed in one group. In this study we were interested in finding a method that could shorten the path to finding participants. Therefore, the use of clustering algorithms to group similar participants was put to test in order to investigate the effectiveness and feasibility of this approach. The clustering algorithms that were used for this study were K-means and DBSCAN. The results showed that the use of these algorithms, for the mentioned purpose, is feasible and that both algorithms can identify similar participants and place them in the same group while participants who are not similar to others, and therefore are not the correct subject of the study, are recognised. Copyright 2012 ACM.
The number of scientific publications published online has increased significantly in tandem with the rise of the Internet. The quantity of online articles has significantly expanded in recent years. As the quantity o...
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An optical modulation format identification technique is proposed based on signal amplitude features and clustering algorithms. Successful classification among five different polarization-multiplexed modulation signal...
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This paper presents Wind Turbine Power Curve (WTPC) modeling based on hybrid fuzzy clustering algorithms and cubic spline. One of the advantages in using fuzzy clustering algorithms is their capability to deal with un...
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Field Programmable Gate Arrays (FPGAs) have become a popular medium for the implementation of many digital circuits. Mapping applications into FPGAs requires a set of efficient Computer-Aided Design (CAD) tools to obt...
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Peer-to-Peer (p2p) networks are used by millions for searching content. Recently, clustering algorithms were shown to be useful for helping users find content in such networks. However, p2p networks often exhibit powe...
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clustering algorithm is one of the most popular data analysis technique in machine learning to precisely evaluate the vast number of healthcare data from the body sensor networks, internet of things devices, hospitals...
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A hierarchical lossy image set compression algorithm (HMST α) has recently been proposed for lossy compression of image sets. It was shown that this algorithm performs well when an image set contains well separated c...
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ISBN:
(纸本)9781601321190
A hierarchical lossy image set compression algorithm (HMST α) has recently been proposed for lossy compression of image sets. It was shown that this algorithm performs well when an image set contains well separated clusters of similar images. As a result, if one applies the HMSTα algorithm after a clustering algorithm has been applied, the compression performance depends on the quality of the partition. In this paper, we examine a number of well-known hierarchical clustering methods and cluster validity measures, and their relationships to the compression performance of HMSTα. This relationship can be used as a component in a fully automated image set compression algorithm. We also briefly examine the merit of using different compression schemes depending on the compactness of the cluster in order to reduce computational complexity.
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