In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and 3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k needs to be set by the user of the algorithms. (Kang 2013) recently proposed a novel use of determinantal point processes for sampling the initial centroids for the KMEANS a...

The k-means++ seeding algorithm is one of the most popular algorithms that is used for finding the initial k centers when using the k-means heuristic. The algorithm is a simple sampling procedure and can be described as follows: Pick the first center randomly from among the given points. For i > 1, pick a point to be the i center with probability proportional to the square of the Euclidean dist...

Resumen. Sin lugar a duda el algoritmo K-means es el más utilizado en la comunidad de aprendizaje no supervisado. Desafortunadamente es muy sensible a la selección de los centroides iniciales. Debido a ello, se han propuesto un gran número de métodos para la selección de los centros iniciales. En este artículo se presenta un algoritmo de agrupamiento que tiene como base al algoritmo K-means, en...

Utilizing the sample size of a dataset, the random cluster model is employed in order to derive an estimate of the mean number of K-Means clusters to form during classification of a dataset.

Clustering sets of histograms has become popular thanks to the success of the generic method of bag-of-X used in text categorization and in visual categorization applications. In this paper, we investigate the use of a parametric family of distortion measures, called the α-divergences, for clustering histograms. Since it usually makes sense to deal with symmetric divergences in information retr...

We run experiments showing that algorithm clarans (Ng et al., 2005) finds better Kmedoids solutions than the standard algorithm. This finding, along with the similarity between the standard K-medoids and K-means algorithms, suggests that clarans may be an effective K-means initializer. We show that this is the case, with clarans outperforming other popular seeding algorithms on 23/23 datasets w...

Much work has sought to discern the different types of cloud regimes, typically via Euclidean k-means clustering of histograms. However, these methods ignore the underlying similarity structure of cloud types. Wasserstein k-means clustering is a promising candidate for utilizing this structure during clustering, but existing algorithms do not scale well and lack the quality guarantees of the Eu...

Recently, Auersperg et al. [1] demonstrated that Goffin’s cockatoos (Cacatua goffini) are able to solve complex means-means-end problems. This is an impressive cognitive ability and it is desirable to build models to understand such abilities. In this paper we describe a project that models such behavior and recreates the experiment on a robotic platform. First preliminary results suggest that ...

در این مقاله، به‌منظور رفع برخی محدودیت‌های شناسایی ساختمان تصاویر چندطیفی، از دادة SAR به‌منزلة مکمل استفاده می‌شود. روش پیشنهادی، برای استفادة هم‌زمان اطلاعات مفید رادار و استراتژی مبتنی‌بر تلفیق تصاویر، با هدف ساختمان، مطرح همچنین، ازآن‌جاکه انتخاب ویژگی نقش بسزایی طبقه‌بندی عوارض دارد، اغلب روش‌های مرسوم رایج زمینه، مانند الگوریتم ژنتیک، نیازمند داده‌های آموزشی‌اند؛ اما دردسترس‌نبودن همیشگی...