Iterative Information Granulation for Novelty Detection in Complex Datasets

Recognition memory in a number of mammals is usually utilised to identify novel objects that violate model predictions. In humans in particular, the recognition of novel objects is foremost associated to their ability to group objects that are highly compatible/similar. Granular computing not only mimics the human cognition to draw objects together but also mimics the ability to capture associated properties by similarity, proximity or functionality. In this paper, an iterative information granulation approach is presented, for the problem of novelty detection in complex data. Two granular compatibility measures are used, based on principles of Granular Computing, namely the multidimensional distance between the granules, as well as the granular density and volume. A two-stage iterative information granulation is proposed in this work. In the first stage, a predefined number of granular detectors are constructed. The granular detectors capture the relationships (rules) between the input-output data and then use this information in a second granulation stage in order to discriminate new samples as novel. The proposed iterative information granulation approach for novelty detection is then applied to three different benchmark problems in pattern recognition demonstrating very good performance

Tracking of magnetic flux concentrations over a five-day observation and an insight into surface magnetic flux transport

The solar dynamo problem is the question of how the cyclic variation in the solar magnetic field is maintained. One of the important processes is the transport of magnetic flux by surface convection. To reveal this process, the dependence of the squared displacement of magnetic flux concentrations upon the elapsed time is investigated in this paper via a feature-recognition technique and a continual five-day magnetogram. This represents the longest time scale over which a satellite observation has ever been performed for this problem. The dependence is found to follow a power-law and differ significantly from that of diffusion transport. Furthermore there is a change in the behavior at a spatial scale of 10^{3.8} km. A super-diffusion behavior with an index of 1.4 is found on smaller scales, while changing to a sub-diffusion behavior with an index of 0.6 on larger ones. I interpret this difference in the transport regime as coming from the network-flow pattern.Comment: 18 pages, 9 figures, accepted for publication in the Journal of Space Weather and Space Climate (SWSC

Examining Granular Computing from a Modeling Perspective

In this paper, we use a set of unified components to conduct granular modeling for problem solving paradigms in several fields of computing. Each identified component may represent a potential research direction in the field of granular computing. A granular computing model for information analysis is proposed. The model may suggest that granular computing is an instrument for implementing perception based computing based on numeric computing. In addition, a novel granular language modeling technique is proposed for information extraction from web pages. This paper also suggests that the study of data mining in the framework of granular computing may address the issues of interpretability and usage of discovered patterns

Model granularity and related concepts

Models are integral to engineering design and basis for many decisions. Therefore, it is necessary to comprehend how a model’s properties might influence its behaviour. Model granularity is an important property but has so far only received limited attention. The terminology used to describe granularity and related phenomena varies and pertinent concepts are distributed across communities. This article positions granularity in the theoretical background of models, collects formal definitions for relevant terms from a range of communities and discusses the implications for engineering design