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Water Drop Algorithm
Published in Nazmul Siddique, Hojjat Adeli, Nature-Inspired Computing, 2017
Clustering is the task of assigning a set of objects into homogeneous groups (called clusters) so that the objects in the same cluster are more similar to each other than to those in other clusters based on a similarity metric. In general, clustering algorithms aim to minimize within-cluster variation (i.e., intracluster distance) and maximize the between-cluster variation (i.e., intercluster distance). Some mathematical description of clustering has been discussed in Chapter 2 in Equations 2.93 through 2.96. The K-means clustering algorithm is a partitioning clustering algorithm and also known as the generalized Lloyd algorithm. In K-means clustering, the number of clusters K is given, K cluster centers are randomly chosen from the data set or randomly generated, and the algorithm partitions the data set into K clusters by minimizing an objective function.
Unsupervised Models
Published in Chandrasekar Vuppalapati, Democratization of Artificial Intelligence for the Future of Humanity, 2021
Looking at these notes time complexity of Lloyds algorithm for k-means clustering is given as: O(n * K * I * d) where n = number of pointsK = number of clustersI = number of iterationsd = number of attributes
Improving Location Services via Social Collaboration
Published in Kaikai Liu, Xiaolin Li, Mobile SmartLife via Sensing, Localization, and Cloud Ecosystems, 2017
Kaikai Liu, Qiuyuan Huang, Jiecong Wang, Xiaolin Li, Dapeng Oliver Wu
The K-means algorithm, a.k.a. the Lloyd algorithm is based on the nearest neighbor criterion and the centroid criterion. However, the K-means algorithm cannot be used for grouping smartphones if the GPS positions of the smartphones are not available due to significant signal attenuation in indoor environments, high rise building environments, or dense forest.
An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops
Published in International Journal of Production Research, 2020
Chaoyang Zhang, Zhengxu Wang, Kai Ding, Felix T.S. Chan, Weixi Ji
The k-means algorithm, also known as Lloyd’s algorithm (Kanungo et al. 2002), uses an iterative refinement technique for clustering. In this study, the input data for the k-means algorithm are energy consumption data of processes , and the goal is to cluster the energy consumption data set into clusters and minimise the within-cluster sum of squares to achieve the least distance of each input energy consumption data set in its cluster to its cluster centre. The total distance of each data set can be defined as: