Efficient mining of salinity and temperature association rules from ARGO data

Huang, Yo-Ping; Kao, Li-Jen; Sandnes, Frode Eika

doi:10.1016/j.eswa.2007.06.007

Cited by 38 publications

(16 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work introduced in Huang et al (2008) mined ocean data time series in order to discover relationship between salinity and temperature variations. Concretely, the authors discovered spatio-temporal patterns from the aforementioned variables and reported QAR using PrefixSpan and FITI algorithms (Pei et al 2001;Tung et al 2003).…”

Section: Codification Of the Individualsmentioning

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

Section: Codification Of the Individualsmentioning

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

show abstract

“…Some other documents also reduce the number of database scans by predefining an efficient data structure, e.g., Tsay and Chiang created cluster-based tables to find frequent itemsets, and for kfrequent itemsets, the number of database scans is less than k [16]; Wu and Huang (2011) defined the frequent closed enumeration table to store maximal itemsets to reduce database scans [17]; and Liu et al (2012) used the directed itemsets graph to store the information of frequent itemsets, which realizes scanning a database only once [18]. In addition, for dealing with raster-formatted datasets, the above mining algorithms are extended with spatial regions, e.g., the spatial clusters [19], objectoriented technologies [20][21][22][23], and event-coverage domains [24]. Since large numbers of grid pixels are replaced by typical regions and thereby simplify the mining process, however, these techniques result in a loss of large amounts of spatial information.…”

Section: Introductionmentioning

confidence: 99%

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

Xue

Liu

2017

IJGI

View full text Add to dashboard Cite

Abstract:The ENSO (El Niño Southern Oscillation) is the dominant inter-annual climate signal on Earth, and its relationships with marine environments constitute a complex interrelated system. As traditional methods face great challenges in analyzing which, how and where marine parameters change when ENSO events occur, we propose an ENSO-oriented marine spatial association pattern (EOMSAP) mining algorithm for dealing with multiple long-term raster-formatted datasets. EOMSAP consists of four key steps. The first quantifies the abnormal variations of marine parameters into three levels using the mean-standard deviation criteria of time series; the second categorizes La Niña events, neutral conditions, or El Niño events using an ENSO index; then, the EOMSAP designs a linking-pruning-generating recursive loop to generate (m + 1)-candidate association patterns from an m-dimensional one by combining a user-specified support with a conditional support; and the fourth generates strong association patterns according to the userspecified evaluation indicators. To demonstrate the feasibility and efficiency of EOMSAP, we present two case studies with real remote sensing datasets from January 1998 to December 2012: one considers performance analysis relative to the ENSO-Apriori and Apriori methods; and the other identifies marine spatial association patterns within the Pacific Ocean.

show abstract

“…These techniques are typically inductive, as opposed to deductive, in that they are not used to prove or disprove pre-existing hypotheses, but rather are used to identify patterns embedded in data, and thereby support hypothesis generation [15]. Association rule mining techniques have been widely used to obtain the interrelationships among geographical parameters, including the interrelationship between bird species richness and geographical parameters [31], the spatial distribution of aerosol optical depth and its affecting factors [32], the spatial co-location patterns between the fish distribution and marine parameters [33], and the teleconnection among regional or global marine parameters [27,30,34].…”

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing Products to Identify Marine Association Patterns in Factors Relating to ENSO in the Pacific Ocean

Xue

Fan

Qian

et al. 2017

IJGI

View full text Add to dashboard Cite

El Niño-Southern Oscillation (ENSO) and its relationships with marine environmental parameters comprise a very complicated and interrelated system. Traditional spatiotemporal techniques face great challenges in dealing with which, how, and where the marine environmental parameters in different zones help to drive, and respond to, ENSO events. Remote sensing products covering a 15-year period from 1998 to 2012 were used to quantitatively explore these patterns in the Pacific Ocean (PO) by a prevail quantitative association rule mining algorithm, that is, a priori, within a mining framework. The marine environmental parameters considered were monthly anomaly of sea surface chlorophyll-a (CHLA), monthly anomaly of sea surface temperature (SSTA), monthly anomaly of sea level anomaly (SLAA), monthly anomaly of sea surface precipitation (SSPA), and monthly anomaly of sea surface wind speed (WSA). Four significant discoveries are found, namely: (1) Association patterns among marine environmental parameters and ENSO events were found primarily in five sub-regions of the PO: the western PO, the central and eastern tropical PO, the middle of the northern subtropical PO, offshore of the California coast, and the southern PO; (2) In the western and the middle and east of the equatorial PO, the association patterns are more complicated than other regions; (3) The following factors were found to be predicators of and responses to La Niña events: abnormal decrease of SLAA and WSA in the east of the equatorial PO, abnormal decrease of SSPA and WSA in the middle of the equatorial PO, abnormal decrease of SSTA in the eastern and central tropical PO, and abnormal increase of SLAA in the western PO; (4) Only abnormal decrease of CHLA in the middle of the equatorial PO was found to be a predicator of and response to El Niño events. These findings will help to improve our abilities to identify the marine association patterns in factors relating to ENSO events.

show abstract

Efficient mining of salinity and temperature association rules from ARGO data

Cited by 38 publications

References 17 publications

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

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

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

Using Remote Sensing Products to Identify Marine Association Patterns in Factors Relating to ENSO in the Pacific Ocean

Contact Info

Product

Resources

About