'Streams',stream); The input argument 'mlfg6331_64' of RandStream specifies to use the multiplicative lagged Fibonacci generator algorithm. options is a structure array with fields that specify options for cont
In this algorithm, we also suggest to apply swarm intelligence techniques for the incremental processing of this new challenging data type. The main novelty of this research work resides on the adaptation of CL-AntInc to perform clustering binary data streams and building growing graphs increasingly...
2.2.1.6 FeatureWeighting k-Means 2.2.2 Algorithms for Text Data 2.2.2.1 Term Frequency (TF) 2.2.2.2 Inverse Document Frequency (IDF) 2.2.2.3 Term Frequency-Inverse Document Frequency (TF-IDF) 2.2.2.4 Chi Square Statistic 2.2.2.5 Frequent Term-Based Text Clustering ...
Visual Data-Mining Techniques 43.5 Clustering Clustering is the process of finding a partitioning of the dataset into homogeneous subsets called clusters. Unlike classification, clustering is unsupervised learning. This means that the classes are unknown and no training set with class labels is avail...
The size of the set L is a parameter to this method, and we use a relative size \(\vert L\vert =l\cdot \vert X\vert\) to accommodate for different data set sizes. For our work, we evaluate the original approach with binary labeling; see the method KMeans−− in Table 1. We...
A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as k-center, k-median, and k-means. Such algorithms need to be both time and and space efficient. In this paper, we address the problem of correlation clustering in the ...
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The most well-known partitional clustering algorithm is k-means. • In model-based clustering algorithms, the cluster model is specified a priori, such as a mixture of Gaussians, or a hidden Markov model (HMM). The model structure (e.g., the number of hidden states in an HMM) can ...
the big data setting. The results obtained show a very good scalability of the algorithm, and its improvement over the current state-of-the-art parallel clustering algorithm, i.e., k-means∥. Furthermore, if the application scenario being analyzed does not require all the features offered by...
Accordingly, analysis of events as discussed herein may be used by, for example, anti-malware security researchers, white-hat vulnerability researchers, interoperability developers, anti-piracy testers, or other analysts of data streams. In some examples, the described techniques are used to detect, ...