The ubiquitous self-organizing map for non-stationary data streams

Abstract

IntroductionAt present, all kinds of stream data processing based on instantaneous data have become critical issues of Internet, Internet of Things (ubiquitous computing), social networking and other technologies. The massive amounts of data being generated in all these environments push the need for algorithms that can extract knowledge in a readily manner.Within this increasingly important field of research the application of artificial neural networks to such task remains a fairly unexplored path. The self-organizing map (SOM) [1] is an unsupervised neural-network algorithm with topology preservation. The SOM has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics [2] over the years. The powerful visualization techniques for SOM models result from the useful and unique feature of SOM for detection of emergent complex cluster structures and non-linear relationships in the feature space [3]. The SOM can be visualized as a sheet-like neural network array, whose neurons become specifically tuned to various input vectors (examples) in an orderly fashion. For instance, the SOM and K-means both represent data in a similar way through prototypes of data, i.e., centroids in K-means and neuron weights in SOM, and their relation AbstractThe Internet of things promises a continuous flow of data where traditional database and data-mining methods cannot be applied. This paper presents improvements on the Ubiquitous Self-Organized Map (UbiSOM), a novel variant of the well-known Self-Organized Map (SOM), tailored for streaming environments. This approach allows ambient intelligence solutions using multidimensional clustering over a continuous data stream to provide continuous exploratory data analysis. The average quantization error and average neuron utility over time are proposed and used to estimating the learning parameters, allowing the model to retain an indefinite plasticity and to cope with changes within a multidimensional data stream. We perform parameter sensitivity analysis and our experiments show that UbiSOM outperforms existing proposals in continuously modeling possibly non-stationary data streams, converging faster to stable models when the underlying distribution is stationary and reacting accordingly to the nature of the change in continuous real world data streams. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. and different usages has already been studied [4]. However, it is the topological ordering of these prototypes in large SOM networks that allows the application of exploratory visualization techniques.This paper is an extended version of work published in [5], introducing a novel variant of SOM, called the ubiquitous self-organizing map (UbiSOM), specially tailored for streaming and big data. We extend our previous work by improving the overall algorithm...

Analysis of a token density metric for concern detection in Matlab sources using UbiSOM

“…The data analyst can continuously change the relevance of each projection axis by visually dragging the projection weight of each concern. Also important is the continuous monitoring of two UbiSOM error measures: the average topological error and average quantization error presented in Silva and Marques and Silva . Both are continuously updated and presented to the data analyst regarding the last 5,000 UbiSOM iterations by using two standard error graphs.…”

“…The UbiSOM is a variant of the well-known Self-Organising Map (SOM; Kohonen, 2001) algorithm. Whereas the latter was conceived for static data, the former is tailored for real-time analysis of streaming data (Silva & Marques, 2015).…”

“…This article presents a Software Engineering Business Intelligence and Knowledge Discovery approach for advanced exploratory data analysis using the Ubiquitous Self‐Organising Map (UbiSOM; Silva & Marques, ). Human–computer interaction is used for tunning UbiSOM to the analysis of a source code base, resulting in an expert systems technique for applying concern mining to study manifestations of lack of modularity support in Matlab code bases (m‐files).…”