A. A. A. Alazeez, S. Jassim and H. Du, “SLDPC: Towards Second Order Learning for Detecting Persistent Clusters in Data Streams,” 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, United Kingdom, 2018, pp. 248-253.


The main attention of research on data stream clustering algorithms so far has been focused on the adaptation of the algorithms for static datasets to the data streams and improvements of the existing adapted algorithms. Such algorithms fulfil the purpose of the first-order learning from data to clusters. This paper prompts a new question on second-order learning of cluster models from data streams and presents a learning algorithm that detects persistent clusters from consecutive clustering snapshots in data streams. In this work, we first collect a sequence of cluster snapshots as the output clusters at selected query points and then identify the persistent clusters within a given timeframe. The algorithm is evaluated on collections of synthetic datasets. The experimental results have demonstrated the effectiveness of the algorithm in detecting such persistent clusters.


Clustering algorithms; Stability analysis; Adaptation models; Object recognition; Gaussian distribution; Data models; Merging; Keywords-Data Stream Clustering Algorithms; Persistent clusters; Clustering of Clusters; Second-order Learning
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