A sliding windows based dual support framework for discovering emerging trends from temporal data

Khan, M. Sulaiman; Coenen, Frans; Reid, David; Patel, Ravindra; Archer, Lewis

doi:10.1016/j.knosys.2009.11.005

Cited by 20 publications

(15 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research described in this work also borrows from the field of Jumping and Emerging Patten (JEP) mining as first introduced by Dong and Li ( [4]). The distinction between the work on JEPs, and that described in this paper, is that JEPs are patterns whose frequency increases (typically) between two data sets (although some work has been done on identifying JEPs across multiple data sets, for example Khan et al [8]). JEP mining is usually also conducted in the context of classification (see for example [5]).…”

Section: Temporal Pattern Miningmentioning

confidence: 99%

Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy

Somaraki

Broadbent

Coenen

et al. 2010

Advances in Data Mining. Applications and Theoretical Aspects

View full text Add to dashboard Cite

Abstract. This paper describes an approach to temporal pattern mining using the concept of user defined temporal prototypes to define the nature of the trends of interests. The temporal patterns are defined in terms of sequences of support values associated with identified frequent patterns. The prototypes are defined mathematically so that they can be mapped onto the temporal patterns. The focus for the advocated temporal pattern mining process is a large longitudinal patient database collected as part of a diabetic retinopathy screening programme, The data set is, in itself, also of interest as it is very noisy (in common with other similar medical datasets) and does not feature a clear association between specific time stamps and subsets of the data. The diabetic retinopathy application, the data warehousing and cleaning process, and the frequent pattern mining procedure (together with the application of the prototype concept) are all described in the paper. An evaluation of the frequent pattern mining process is also presented.

show abstract

Section: Temporal Pattern Miningmentioning

confidence: 99%

Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy

Somaraki

Broadbent

Coenen

et al. 2010

Advances in Data Mining. Applications and Theoretical Aspects

View full text Add to dashboard Cite

show abstract

“…However, this concept has been recently used in (Khan et al 2010) with the purpose of obtaining a low use of memory and low proposed the use of some new operators such as rounding, repairing or filtrating. Finally, QAR have also been used in the bioinformatics field.…”

Section: Related Workmentioning

confidence: 99%

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

Martínez-Ballesteros¹,

Martínez‐Álvarez²,

Troncoso³

et al. 2011

Soft Comput

View full text Add to dashboard Cite

An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative association rules. This algorithm, called QARGA (Quantitative Association Rules by Genetic Algorithm), uses a particular codification of the individuals that allows solving two basic problems. First, it does not perform a previous attribute discretization and, second, it is not necessary to set which variables belong to the antecedent or consequent. Therefore, it may discover all underlying dependencies among different variables. To evaluate the proposed algorithm three experiments have been carried out. As initial step, several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches. Also, the algorithm has been applied to synthetic time series (where the relationships are known) to analyze its potential for discovering rules in time series. Finally, a real-world multidimensional time series composed by several climatological variables has been considered. All the results show a remarkable performance of QARGA.

show abstract

“…The frequent pattern idea has been extended in many directions. A number of authors have considered the nature of frequent patterns with respect to the temporal dimension, for example sequential patterns [8], frequent episodes [9], emerging patterns [10] and jumping and emerging patterns [3]. Many alternative frequent pattern mining algorithms, that seek to improve on Agrawal's original Apriori algorithm, have also been proposed.…”

Section: Previous Workmentioning

confidence: 99%

Social Network Trend Analysis Using Frequent Pattern Mining and Self Organizing Maps

Nohuddin

Christley

Coenen

et al. 2010

Research and Development in Intelligent Systems XXVII

View full text Add to dashboard Cite

A technique for identifying, grouping and analysing trends in social networks is described. The trends of interest are defined in terms of sequences of support values for specific patterns that appear across a given social network. The trends are grouped using a SOM technique so that similar trends are clustered together. A cluster analysis technique is then applied to identify "interesting" trends. The focus of the paper is the Cattle Tracing System (CTS) database in operation in Great Britain, and this is therefore the focus of the evaluation. However, to illustrate the wider applicability of the trend mining technique, experiments using a more standard, car insurance, temporal database are also described.

show abstract

A sliding windows based dual support framework for discovering emerging trends from temporal data

Cited by 20 publications

References 11 publications

Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy

Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

Social Network Trend Analysis Using Frequent Pattern Mining and Self Organizing Maps

Contact Info

Product

Resources

About