The 2012 International Joint Conference on Neural Networks (IJCNN) 2012
DOI: 10.1109/ijcnn.2012.6252680
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A new Granular Computing approach for sequences representation and classification

Abstract: In this paper we present an innovative procedure for sequence mining and representation. It can be used as its own in Data Mining problems or as the core of a classification system based on a Granular Computing approach to represent sequences in a suited embedding space. By adopting an inexact sequence matching procedure, the algorithm is able to extract a symbols alphabet of frequent subsequences to be used as prototypes for the embedding stage. Experimental evaluation over both synthetically generated and bi… Show more

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Cited by 23 publications
(17 citation statements)
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“…In this Section we describe in detail the structure of the whole classification system GRASC-F, focusing our attention on all the aspects that concern the financial prediction task. The system GRASC-F is an evolution of GRAPSEC (Rizzi et al, 2013), a sequences classification system based on GrC algorithms GRADIS (Livi et al, 2012) and RL-GRADIS (Rizzi et al, 2012). These are data mining procedures able to discover consistent patterns of variable-length that occur frequently in a data set.…”
Section: The Grasc-f Systemmentioning
confidence: 99%
“…In this Section we describe in detail the structure of the whole classification system GRASC-F, focusing our attention on all the aspects that concern the financial prediction task. The system GRASC-F is an evolution of GRAPSEC (Rizzi et al, 2013), a sequences classification system based on GrC algorithms GRADIS (Livi et al, 2012) and RL-GRADIS (Rizzi et al, 2012). These are data mining procedures able to discover consistent patterns of variable-length that occur frequently in a data set.…”
Section: The Grasc-f Systemmentioning
confidence: 99%
“…• the Symbols Alphabet Extraction, which addresses the problem of finding the most frequent subsequences within a SDB. It is performed by means of the clustering algorithm RL-GRADIS (Rizzi et al, 2012) that identifies frequent subsequences as representatives of dense clusters of similar subsequences. These representatives are referred to as symbols and the pattern set as the alphabet.…”
Section: The Mining Algorithmmentioning
confidence: 99%
“…Most methods focus only on the recurrence of patterns in data without taking into account the concept of "information redundancy", or, in other words, the existence of overlapping among retrieved patterns. In this paper we present a new approximate subsequence mining algorithm called FRL-GRADIS (Filtered Reinforcement Learning-based GRanular Approach for DIscrete Sequences) aiming to reduce the information redundancy of RL-GRADIS (Rizzi et al, 2012) by executing an optimization-based refinement process on the extracted patterns. In particular, this paper introduces the following contributions:…”
Section: Introductionmentioning
confidence: 99%
“…The concept of Granular Computing arose from many branches of natural and social sciences [16][17][18] and it is at the basis of recently developed frameworks in computational intelligence [19][20][21]. In this context, an ASM technique that identifies frequent and meaningful motifs in sequences database allows to design advanced machine learning systems such as Symbolic Histograms approaches [22][23][24], where each pattern (a sequence of objects/events) can be represented by a histogram of motif instances.…”
Section: Introductionmentioning
confidence: 99%