Visual Analysis of Sequences Using Fractal Geometry

Abstract

SummarySequence analysis is a challenging task in the data mining arena, relevant for many practical domains. We propose a novel method for visual analysis and classification of sequences based on Iterated Function System (IFS). IFS is utilized to produce a fractal representation of sequences. The proposed method offers an effective tool for visual detection of sequence patterns influencing a target attribute, and requires no understanding of mathematical or statistical algorithms. Moreover, it enables to detect sequence patterns of any length, without predefining the sequence pattern length. It also enables to visually distinguish between different sequence patterns in cases of reoccurrence of categories within a sequence. Our proposed method provides another significant added value by enabling the visual detection of rare and missing sequences per target class.