一种具有特定K簇的多均值聚类方法”的实现.zip_K._k-means聚类算法_聚类

# KDD2019_K-Multiple-Means (KMM) Implementation for the paper "K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters", which has been accepted by KDD'2019 as an Oral Paper, in the Research Track. Paper: https://dl.acm.org/citation.cfm?id=3330846 ## Abstract In this paper, we make an extension of K-means for the clustering of multiple means. The popular K-means clustering uses only one center to model each class of data. However, the assumption on the shape of the clusters prohibits it to capture the non-convex patterns. Moreover, many categories consist of multiple subclasses which obviously cannot be represented by a single prototype. We propose a K-Multiple-Means (KMM) method to group the data points with multiple sub-cluster means into specified k clusters. Unlike the methods which use the agglomerative strategies, the proposed method formalizes the multiple-means clustering problem as an optimization problem and updates the partitions of m subcluster means and k clusters by an alternating optimization strategy. Notably, the partition of the original data with multiple-means representation is modeled as a bipartite graph partitioning problem with the constrained Laplacian rank. We also show the theoretical analysis of the connection between our method and the K-means clustering. Meanwhile, KMM is linear scaled with respect to n. Experimental results on several synthetic and well-known realworld data sets are conducted to show the effectiveness of the proposed algorithm. ## Short demo Run 'test_KMM_toy.m' in MATLAB. A example of updating process of multiple-means is as follows: <img src="https://github.com/CHLWR/KDD2019_K-Multiple-Means/blob/master/demo4.gif" align="center" width="400" height="400" /> ## Short Promotion Video Welcome to click 'thumbs-up' for our work. https://youtu.be/HswEYH2td8w [](https://www.youtube.com/embed/HswEYH2td8w "K-Multiple-Means") ## Oral Presentation Video which will be released soon. ## Others KMM can not only obtain the clustering result but also obtain the prototypes corresponding to each sub-cluster, which can be applied to many fields such as vector quantization, cluster analysis, feature learning, nearest-neighbor search, data compression, etc. KMM can also be used as an anchor-based spectral clustering method, which can find better anchors and achieve better clustering result. If you find this code useful in your research, please cite the paper. ## Reference: Feiping Nie, Cheng-Long Wang, Xuelong Li, "K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters," in *Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19)*, Anchorage, AK, USA, August 4–8, 2019. ---- ## Update Log Fixed a bug on random seeds generation! Oct-12th-2019