Package: hartigan-kmeans
Hartigan's K-MeansScopeThis project provides an efficient implementation of Hartigan’s method for k-means clustering (Hartigan 1975). It builds on the work ofSlonim, Aharoni and Crammer (2013), which introduced a significant improvement to the algorithm computational complexity, and adds an additional optimization for inputs in sparse vector representation. The project is packaged as a python library with a cython-wrapped C++ extension for the partition optimization code. A pure python implementation is included as well.Installationpip install hartigan-kmeansUsageThe main class in this library isHKmeans, which implements the clustering interface ofSciKit Learn, providing methods such asfit(),fit_transform(),fit_predict(), etc.The sample code below clusters the 18.8K documents of the 20-News-Groups dataset into 20 clusters:importnumpyasnpfromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.datasetsimportfetch_20newsgroupsfromsklearnimportmetricsfromhkmeansimportHKMeans# read the datasetdataset=fetch_20newsgroups(subset='all',categories=None,shuffle=True,random_state=256)gold_labels=dataset.targetn_clusters=np.unique(gold_labels).shape[0]# create count vectors using the 10K most frequent wordsvectorizer=TfidfVectorizer(max_features=10000)X=vectorizer.fit_transform(dataset.data)# HKMeans initialization and clustering; parameters:# perform 10 random initializations (n_init=10); the best one is returned.# up to 15 optimization iterations in each initialization (max_iter=15)# use all cores in the running machine for parallel execution (n_jobs=-1)hkmeans=HKMeans(n_clusters=n_clusters,random_state=128,n_init=10,n_jobs=-1,max_iter=15,verbose=True)hkmeans.fit(X)# report standard clustering metricsprint("Homogeneity:%0.3f"%metrics.homogeneity_score(gold_labels,hkmeans.labels_))print("Completeness:%0.3f"%metrics.completeness_score(gold_labels,hkmeans.labels_))print("V-measure:%0.3f"%metrics.v_measure_score(gold_labels,hkmeans.labels_))print("Adjusted Rand-Index:%.3f"%metrics.adjusted_rand_score(gold_labels,hkmeans.labels_))print("Adjusted Mutual-Info:%.3f"%metrics.adjusted_mutual_info_score(gold_labels,hkmeans.labels_))Expected result:Homogeneity: 0.245
Completeness: 0.290
V-measure: 0.266
Adjusted Rand-Index: 0.099
Adjusted Mutual-Info: 0.263See theExamplesdirectory for more illustrations and a comparison against Lloyd's K-Means.LicenseCopyright IBM Corporation 2021

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.If you would like to see the detailed LICENSE clickhere.AuthorsAlgorithm:Hartigan 1975Pseudo-code and optimization:Slonim, Aharoni and Crammer (2013)Programming, optimization and maintenance:Assaf ToledoIf you have any questions or issues you can create a newissue here.ReferencesHartigan, John A. Clustering algorithms. Wiley series in probability and mathematical statistics: Applied probability and statistics. John Wiley & Sons, Inc., 1975.Slonim, Noam, Ehud Aharoni, and Koby Crammer. "Hartigan's K-Means Versus Lloyd's K-Means—Is It Time for a Change?." Twenty-Third International Joint Conference on Artificial Intelligence. 2013.