This paper reflects the results of an implementation of the K-means algorithm on U.N survey data on people’s priorities, organized by country. The dataset includes 16 features for each country, with each feature corresponding to a different societal issue. Each country has a rating in the range of [0, 1] that indicates how important a particular feature or issue is to that country’s people– the...

A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...

This paper shows that one can be competitive with the kmeans objective while operating online. In this model, the algorithm receives vectors v1, . . . , vn one by one in an arbitrary order. For each vector vt the algorithm outputs a cluster identifier before receiving vt+1. Our online algorithm generates Õ(k) clusters whose k-means cost is Õ(W ∗) where W ∗ is the optimal k-means cost using k cl...

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...

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...

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...

We present a new clustering algorithm called k-means-u* which in many cases is able to significantly improve the clusterings found by k-means++, the current de-facto standard for clustering in Euclidean spaces. First we introduce the k-means-u algorithm which starts from a result of k-means++ and attempts to improve it with a sequence of non-local “jumps” alternated by runs of standard k-means....

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 the given points. For i > 1, pick a point to be the i center with probability proportional to the square of the Euclidean distance o...

The K-Means algorithm for clustering has the drawback of always maintaining K clusters. This leads to ineffective handling of noisy data and outliers. Noisy data is defined as having little similarity with the closest cluster’s centroid. In K-Means a noisy data item is placed in the most similar cluster, despite this similarity is low relative to the similarity of other data items in the same c...