The k-means clustering algorithm, whilst widely popular, is not without its drawbacks. In this paper, we focus on the sensitivity of to initial set centroids. Since cluster recovery performance can be improved by better initialisation, numerous algorithms have been proposed aiming at producing good However, it still unclear which algorithm should used in any particular scenario. With mind, comp...

Dealing with distributed data is one of the challenges for clustering, as most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one of the most influential data mining algorithms for being simple, scalable, and easily modifiable to a variety of contexts and application domains. However, exact distributed versions of k-means are still sensitive ...

Metal yüzeyleri korozyonun etkisinden korumak için organik kaplamalar yaygın olarak kullanılır. Organik ile korozyon korumasının mekanizmaları bilinmesine rağmen, bu mekanizmaların gelişiminin tuzlu sis (ASTM B-117) ve yoğuşma kabini D4585) gibi geleneksel hızlandırılmış maruziyet testleri izlenmesi mümkün değildir. Bu testlerde sadece görsel emarelerin ortaya çıkmasıyla gözlenebilir, durum kap...

ADC data sheets and test methods are not yet standardized. A new attempt to create a common platform for these is the draft standard IEEE-STD-1241. However, the methods described in this standard need an extra effort from the user to exactly understand and implement them. It is therefore very reasonable to provide programs which implement these methods, and allow manufacturers and users to use ...

SOM and k-means are two classical methods for text clustering. In this paper some experiments have been done to compare their performances. The sample data used is 420 articles which come from different topics. K-means method is simple and easy to implement; the structure of SOM is relatively complex, but the clustering results are more visual and easy to comprehend. The comparison results also...

Incremental K-means and DBSCAN are two very important and popular clustering techniques for today‟s large dynamic databases (Data warehouses, WWW and so on) where data are changed at random fashion. The performance of the incremental K-means and the incremental DBSCAN are different with each other based on their time analysis characteristics. Both algorithms are efficient compare to their exist...