Abstract
<h2>Abstract</h2> Currently, many applications work with data streams, such as financial market analysis, detection of attacks on computer network systems, fraud detection, detection and fault identification, social media analysis, e-commerce analysis, and marketing campaign assistance. In this context, approaches based on evolving systems have shown to be quite adequate for analyzing and processing this kind of data. AutoCloud is an evolving algorithm specially designed for dynamic clustering of data stream with online learning. Here, the AutoCloud is applied to fault detection and diagnosis of dynamic systems using online and unsupervised learning.
Highlights
Because data streams are usually obtained in online way, they have unknown size a priori and the statistical distribution of their data can change over time (Silva et al, 2013)
Algorithms for data streams need to be able to adapt in online way to concept drift and concept evolution
We present an implementation of the evolving clustering algorithm called AutoCloud
Summary
Algorithms for data streams need to be able to adapt in online way to concept drift and concept evolution. In this context, evolving based algorithms, which can adapt their struct and parameters in online way, have proven to be very suitable for working with data streams. The AutoCloud is computationally efficient and suitable for real-time applications It leverages ever-evolving cluster-like granular structures - the data clouds - where the parameters of each granule can be adapted, and new clusters can be created and existing ones merged, which makes it suitable for handling dy namic and evolving data (concept drift and concept evolution) (Bezerra et al, 2020). Ayoubi (1994) Zhao et al (2018) Liu et al (2019) Rodrıguez-Ramos et al (2018) Costa et al (2014) Silva et al (2020) Bezerra et al (2016)
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