Grouping and long term prediction of sunspot cycle characteristics-A fuzzy clustering approach

Based on the pattern recognition algorithm called fuzzy c-means clustering, grouping of sunspot cycles has been carried out. It is found that, optimally the sunspot cycles can be divided in to two groups; we name it as Large Group and Small Group. Based on the fuzzy membership values the groups are...

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Bibliographic Details
Published in:Astronomy and computing 2024-07, Vol.48, p.100836, Article 100836
Main Authors: Anilkumar, B.T., Sabarinath, A
Format: Article
Language:English
Subjects:
C-means algorithm
Fuzzy clustering
Sunspot numbers
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Summary:Based on the pattern recognition algorithm called fuzzy c-means clustering, grouping of sunspot cycles has been carried out. It is found that, optimally the sunspot cycles can be divided in to two groups; we name it as Large Group and Small Group. Based on the fuzzy membership values the groups are derived. According to our analysis, cycles 1,5,6,7,12,13,14,15,16 and 24 belongs to the Small class, where as cycles 2,3,4,8,9,10,11,17,18,19,20,21,22, and 23 belongs to the Large class. Based on the features of each group and its fuzzy cluster center, prediction of cycle 25 is also been made. Also on the periodicity of the occurrence of the groups, a new cyclic behaviour has been found for the occurrences of the identical sunspot cycles. According to our study Cycle 25 belongs to small class and further we predict that the future cycle up to cycle 32 may fall in small group.
ISSN:2213-1337
DOI:10.1016/j.ascom.2024.100836