anytime miningmultiport streamsclustering streaming dataClustering of data streams has become very popular in recent times, owing to rapid rise of real-time streaming utilities that produce large amounts of data
Dynamic Clustering Forest (DCF) is a statistical based method Song et al., 2016. It deals with concept drift over the data stream. This method constructs several clustering trees (CTs). Each CT represents the different concepts of the data stream. Here, DCF uses two approaches, namely Discrim...
Title: Incremental classification and clustering, concept drift, novelty detection, active learning in big/fast data context Description: The development of dynamic information analysis methods, like incremental classification/clustering, concept drift management novelty detection techniques and active learning i...
In the case of real-world data streams, the underlying data distribution will not be static; it is subject to variation over time, which is known as the primary reason for concept drift. Concept drift poses severe problems to the accuracy of a model in o
A probability sampling scheme, perhaps using some stratification or clustering based on Z, is clearly ignorable. Nonprobability sampling schemes based on Z (for example selecting exactly those units corresponding to a particular set of values of Z) are also ignorable. However, whether or not ...
Recent work has shown improvements in text clustering and classification by integrating conceptual features extracted from background knowledge. In this paper we address the problem of text classification with labeled data and unlabeled data. We propose a Latent Bayes Ensemble model based on word-...
Hierarchical clustering of the correlation matrix was performed using Ward’s linkage method, to identify groups of related metrics. A total of 270 video segments of 10 s duration were used to assess the relationships between metrics. Ability of metrics to distinguish between behaviours We ...
To fill this gap, this research clustered groups into distinct clusters based on the collaborative discourse data by using agglomerative hierarchical clustering approach, and examined the process characteristics of different clusters and associated performances. Four clusters were identified and labeled. ...
of concept drift detection in order to conduct frequent pattern mining. To address the limitation of fixed sliding windows in adapting to evolving data streams, we propose a variable sliding window frequent pattern mining algorithm, which dynamically adjusts the window size to adapt to new concept ...
the structure of large-scale space. This approach is proposed to solve theSLAMtask in an environment with multiple nested large-scale loops.Cebollada, Payá, Mayol et al. (2019)propose a study aboutclustering methodsto carry out efficiently thedata compactionof metric and topological maps based ...