… So far, we have discussed the application of clustering algorithms on static data. Nevertheless,
when analyzing data, it is important to take into account whether the data are dynamic or …

… Our clustering algorithm works on any ℓp-norm objective (eg k-… our algorithm transforms
any vanilla clustering solution into a fair one incurring only a slight loss in quality. • Our algorithm …

… Clustering is often applied to mixed datasets to find structures and to group similar objects for
further analysis. However, clustering mixed data are … of mixed data clustering algorithms by …

… in the natural classes of data [45]. The standard K-means algorithm clusters the data based
on the properties of the data shared among the data objects, which naturally results in crisp …

… Data clustering Data mining Optimisation based-clustering … the M-ACOC algorithm to solve
data clustering problems has … are most commonly used in the clustering evaluation domain. …

… data clustering algorithms is introduced to address this limitation. Automatic clustering
algorithms refer to any clustering … determine the number of clusters without having any prior …

… reduction, parallel clustering, and MapReduce-based clustering [2]. Meanwhile, partitioned
clustering is a type of data mining algorithmic clustering integrating different algorithms like K-…

… FCM algorithm to make it applicable to data with unequal cluster … Revised FCM (RFCM)
algorithm employs adaptive … the cluster centers and modifies constraint of the FCM algorithm to …

… algorithm can improve the k-prototype algorithm 12. For the second weakness, we propose
using the genetic algorithm to optimize the clustering algorithm so … and Genetic Algorithm to …

… , the monograph focuses on several popular clustering algorithms and groups them according
to … in clustering algorithms for big data and open-source software for cluster analysis. …