CHTree: A Technique for Data Clustering to benefit the Reclustering process based on the Huffman Tree This article propose a novel clustering technique, using Huffman Trees to improve the execution time of reclustering process. The reclustering process is the assignment of data to new clusters when a new number of clusters is needed and occurs after a normal clustering process. The proposed new technique called CHTree (Clustered Huffman Tree) allow sort and group the data in more efficient way, achieving substantial improves in time of reclusterization over the classic clustering methods as Kmeans, with no major impact in the quality of the resultant clusters. This is achieved since the parameters of internal validation are equals in the most cases and are the same for optimal number of clusters. The experimental results allow to conclude that CHTree reach better clustering time compared with Kmeans (and its variant Kmeans) with minimal impact in terms of separation and compactation in datasets with a gaussian distribution and well separated clusters.

A Binary Linear ProgrammingBased KMeans Approach for the Capacitated Centered Clustering Problem The kmeans algorithm is one of the most popular clustering algorithms in the machine learning community. Its simplicity and scalability make it the primary choice for many clustering applications. We introduce here a variant of the kmeans algorithm that can account for complex side constraints. The key idea is to use binary linear programming for assigning objects to clusters. Unlike existing extensions of the kmeans algorithm that are designed for accommodating specific types of constraints, our approach can be applied to a wide range of constrained clustering problems with minor modifications. We demonstrate the effectiveness and efficiency of the proposed approach by comparing it to a stateoftheart algorithm on a test set that comprises 18 instances of the capacitated centered clustering problem. The proposed approach performed particularly well on largesized instances with more than 100 clusters. It even found new bestknown solutions for the four largest instances in the test set.