نتایج جستجو برای: الگوریتم k means

تعداد نتایج: 723945  

Journal: :Discrete & Computational Geometry 2000
Jirí Matousek

ژورنال: :مهندسی مالی و مدیریت اوراق بهادار 2014
ابراهیم پورزرندی مینا کیخا

دیدگاهی که در این مقاله ارائه می دهیم در دو مرحله جای می گیرد: مرحله ی اول طبقه بندی سهم ها ی پورتفوی ابتدایی با روش k-means به دسته های کوچکتر است، سپس طبقه ای که کمترین ریسک و بیشترین بازده را دارد یا به عبارتی طبقه ای که بهینه تر می باشد را به عنوان ورودی الگوریتم خود که آن را minvarmaxr نامیده ایم برمی گزینیم. الگوریتم مذبور،الگوریتم پویایی، براساس الگوریتم ژنتیک و مفهوم ارزش در معرض خطر می...

Journal: :CoRR 2015
Robert A. Murphy

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.

Journal: :CoRR 2016
Marco Capó Aritz Pérez Martínez José Antonio Lozano

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. Even though datasets have grown in size, the K-means algorithm remains as one of the most popular clustering methods, in spite of its dependency on the initial settings and high computational cost, especially in terms of d...

2016
Edo Liberty Ram Sriharsha Maxim Sviridenko

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...

Journal: :PVLDB 2012
Bahman Bahmani Benjamin Moseley Andrea Vattani Ravi Kumar Sergei Vassilvitskii

Over half a century old and showing no signs of aging, k-means remains one of the most popular data processing algorithms. As is well-known, a proper initialization of k-means is crucial for obtaining a good final solution. The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution. A major downside ...

Journal: :Research in Computing Science 2016
Eréndira Rendón Lara Itzel M. Abundez B.

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...

2009
Nir Ailon Ragesh Jaiswal Claire Monteleoni

We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting. We make no assumptions about the data, and our algorithm is very light-weight in terms of memory, and computation. This setting is applicable to unsupervised learning on massive data sets, or resource-constrained devices. The two main ingredients of our theoretical work are: ...

Journal: :CoRR 2014
Apoorv Agarwal Anna Choromanska Krzysztof Choromanski

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...

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