GitHub - serban/kmeans: A CUDA implementation of the k-means clustering algorithm
This software is dervied from Professor Wei-keng Liao's parallel k-means clustering code obtained on November 21, 2010 from http://users.eecs.northwestern.edu/~wkliao/Kmeans/index.html (http://users.eecs.northwestern.edu/~wkliao/Kmeans/simple_kmeans.tar.gz). With his permission, I am publishing my CUDA implementation based on his code under the open-source MIT license. See the LICENSE file for mor
yakmo - C++ implementation of robust, efficient alternative k-means clustering
yakmo - C++ implementation of robust, efficient alternative k-means clustering About Yakmo implements robust, efficient k-means clustering with triangular inequality [1] and smart initialization [2], while supporting alternative clustering outputs [3]. The use of the triangular inequality allows k-means to skip unnecessary distance calculations, while the smart initialization by randomized seeding
K-Means Clustering of Word2Vec on Python | WIRED IN
概要 scikit-learnのK-Means実装を使って、学習済みWord2Vecのクラスタリングを行った。 それなりに上手く、クラスタリングできていそうだった。 はじめに ACL2014で、EmbeddingsのクラスタをNER(Named Entity Recognition)に使用している論文がある1。 線形モデルには、低次元連続値の素性(特徴量)より、高次元離散値の素性が良いらしい。 この記事では、Word2Vecで学習した単語ベクトル表現(連続値)を使って、K-Meansによるクラスタリング(離散値)を行ってみる。 設定 Google Code word2vecにて公開されている、GoogleNews-vectors-negative300.bin.gzを入力に用いた。 PyPIで公開されているword2vecがTypeErrorで上手くモデルファイルをロードできなかったので、
Clustering Algorithms: From Start To State Of The Art | Toptal®
It’s not a bad time to be a Data Scientist. Serious people may find interest in you if you turn the conversation towards “Big Data”, and the rest of the party crowd will be intrigued when you mention “Artificial Intelligence” and “Machine Learning”. Even Google thinks you’re not bad, and that you’re getting even better. There are a lot of ‘smart’ algorithms that help data scientists do their wizar
nicsell: RGP domain backorder service for .de & .eu & .at domains
About nicsell Nicsell is a domain backorder service, also known as dropcatcher, that allows you to bid on several expiring domains. Due to our low starting prices and high success rate we proudly present to you the best price-performance ratio in the German region. Starting from € 10 you will already be able to participate in all our auctions and gain the chance to receive the domains you want. A
Efficient Algorithms for K-Means Clustering
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu This is a collection of C++ procedures for performing k-means clustering based on a combination of local search and Lloyd's algorithm (also known as the k-means algorithm). Given any set of k centers Z, for each center z in Z, let V(z) denote its neighborhood, that is, the set of data points f
k-means clustering - Wikipedia
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cl
Parallel K-Means Data Clustering
This software package parallel-kmeans.tar.gz (4.6 MB) of parallel K-means data clustering contains the followings: A parallel implementation using OpenMP and C A parallel implementation using MPI and C A sequential version in C For large data support (more than 2 billion number of data points), see this page for an MPI implementation that uses 8-byte integers. Algorithm: The above sequential algor
Mahout Programming : K-Means Clustering - hamadakoichi blog
Mahout でのデータマイニング。mahout.clustering.kmeans を使ったクラスター分析を実装してみた。Mahoutは、Hadoop上で動くデータマイニング・機械学習の各種アルゴリズムが実装されているライブラリ。 クラスター分析 クラスター分析の方法論自体の内容は以下のエントリにまとめてある。 R言語プログラミング： クラスター分析 - 階層的クラスタリング - hamadakoichi blog 第２回データマイニング+WEB 勉強会＠東京 (Tokyo.Webmining#2) を開催しました - 「はじめてでもわかる R言語によるクラスター分析」 - hamadakoichi blog 第３回 データマイニング+WEB 勉強会＠東京 (Tokyo.Webmining#3) を開催します - hamadakoichi blog R言語プログラミング： クラスター分析
K-means : An Algorithm for Clustering Data
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Mahout: Canopy and K-means Clustering - hamadakoichi blog
MahoutのCanopyとK-Meansを用い、Canopy生成しCanopy Centroidを用いたK-Means Clustering実行できる Driverの実装法を解説します。次のようなコマンドライン呼び出しで、質の良い Canopy+K-Meansの一連のClusteringの手続きを実行できます。以下では、org.apache.mahout.clustering.canopykmeans packageを作り、追加実装しコンパイルしたJobファイルを $MAHOUT_HOME/bin/mahout-core-0.4-job.jar に置いています。MahoutのVersionは最新の0.4です。 実行例 $HADOOP_HOME/bin/hadoop jar $MAHOUT_HOME/bin/mahout-core-0.4-job.jar \ org.apache.maho
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k-means clustering
k-means clustering is a neat way to find ‘centers of density’ in a group of points. It’s useful for applications like finding natural groups, simplifying visualizations, and summarizing data. There are plenty of libraries that implement the algorithm in Javascript, R and more. While I’ve been working on an implementation with some interesting properties, this article is about understanding the alg
Clustering text documents using k-means — scikit-learn 0.14 documentation
This documentation is for scikit-learn version 0.14 — Other versions If you use the software, please consider citing scikit-learn. Clustering text documents using k-means Clustering text documents using k-means¶ This is an example showing how the scikit-learn can be used to cluster documents by topics using a bag-of-words approach. This example uses a scipy.sparse matrix to store the features inst
Finding the K in K-Means Clustering
A couple of weeks ago, here at The Data Science Lab we showed how Lloyd’s algorithm can be used to cluster points using k-means with a simple python implementation. We also produced interesting visualizations of the Voronoi tessellation induced by the clustering. At the end of the post we hinted at some of the shortcomings of this clustering procedure. The basic k-means is an extremely simple and
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Multi-Threaded K-Means Clustering in .NET 4.0
Google Code Archive - Long-term storage for Google Code Project Hosting.
https://dl.acm.org/citation.cfm?id=1695659.1695733
Web-Scale K-Means Clustering
http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf