Herawan, "Big data clustering: a review," in Proc. Int. Conf. on Computational Science and Its Applications, 2014, pp. 707-720.Shirkhorshidi, Ali Seyed, Saeed Aghabozorgi, Teh Ying Wah, and Tutut Herawan. "Big Data clustering: a review." In International Conference on Computational ...
Data clustering: a review. ACM Comput Surv 来自 ResearchGate 喜欢 0 阅读量: 430 作者:AK Jain,MN Murty,PJ Flynn 摘要: The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental...
To make it possible for the compression method to efficiently compress the data, a promising solution is to apply the clustering method to the input data to divide them into several different groups and then compress these input data according to the clustering information. The compression method ...
Described by an Objective Function: Finds clusters that minimize or maximize an objective function. How to define the 'goodness' of a clustering. Proximity matrix defines a weighted graph, where the nodes are the points being clustered, and the weighted edges represent the proximities between points...
Keeping the current research trends in mind the present paper contributes in the survey of partitional clustering in terms of four aspects: (1) systematic review on all the single objective nature inspired metaheuristics used in partitional clustering, (2) up-to-date survey on flexible partitional ...
Clustering, which has been widely used as a forecasting tool for gene expression data, remains problematic at a very deep level: different initial points of clustering lead to different processes of convergence. However, the setting of initial points is
clustering approaches would be more appropriate (see, e.g., Sander et al.63). We chose four clusters based on the analysis of the within groups similarity for the result of thek-means run for a pre-specified number of clusters from 2 to 15. Figure4shows the resulting day-to-nighttime ...
This split and clustering method help is mitigating redundant transactions held across different nodes. Therefore, communication and redundant computing lowered considerably. FiDoop-DP performs better than PFP (Li et al., 2008, Li et al., 2008) by up to 31% with an average of 18%. Shuffler...
In this paper, we propose a latent feature group learning (LFGL) algorithm to automatically learn the latent feature groups in the process of subspace clustering for high-dimensional data. This algorithm consists of two levels of optimizations. The outer level of optimization uses the Darwinian evo...
Clustering Generative models Low-density separation Laplacian regularization Heuristic approaches Reinforcement Learning Q Learning SARSA (State-Action-Reward-State-Action) algorithm Temporal difference learning Data Mining Algorithms C4.5 k-Means SVM (Support Vector Machine) Apriori EM (Expectation-Maximization...