ApproxMGMSP: A Scalable Method of Mining Approximate Multidimensional Sequential Patterns on Distributed System

Zhang, Changhai; Hu, Kongfa; Chen, Zhuxi; Chen, Ling; Dong, Yuan

doi:10.1109/fskd.2007.192

Cited by 6 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multidimensional sequential pattern mining [33] extracts more useful information than sequential pattern mining as there are various applications use and access multidimensional database. In this paper, the multidimensional sequential patterns are converted to sequential patterns, by embedding the multidimensional information into the corresponding sequences.…”

Section: Approximate Multidimensional Sequential Patterns On Distribumentioning

confidence: 99%

Distributed Sequential Pattern Mining: A Survey and Future Scope

Itkar¹,

Kulkarni²

2014

IJCA

View full text Add to dashboard Cite

Distributed sequential pattern mining is the data mining method to discover sequential patterns from large sequential database on distributed environment. It is used in many wide applications including web mining, customer shopping record, biomedical analysis, scientific research, etc. A large research has been done on sequential pattern mining on various distributed environments like Grid, Hadoop, Cluster, Cloud, etc. Different types of sequential pattern mining can be performed are sequential patterns, maximal sequential patterns, closed sequences, constraint based and time interval based sequential patterns. This paper presents a systematic review on work done for sequential pattern mining and advanced sequential pattern mining on distributed environment. This paper finally presents future research directions related to sequential pattern mining in distributed environment.

show abstract

Section: Approximate Multidimensional Sequential Patterns On Distribumentioning

confidence: 99%

Distributed Sequential Pattern Mining: A Survey and Future Scope

Itkar¹,

Kulkarni²

2014

IJCA

View full text Add to dashboard Cite

show abstract

“…However, no algorithm is defined in this paper. In [Zhang et al 2007], the authors propose the mining of multi-dimensional sequential patterns in distributed systems.…”

Section: Multi-dimensional Sequential Patternsmentioning

confidence: 99%

Mining multidimensional and multilevel sequential patterns

Plantevit

Laurent

et al. 2010

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

International audienceMultidimensional databases have been designed to provide decision makers with the necessary tools to help them understand their data. This framework is different from transactional data as the datasets contain huge volumes of historicized and aggregated data defined over a set of dimensions that can be arranged through multiple levels of granularities. Many tools have been proposed to query the data and navigate through the levels of granularity. However, automatic tools are still missing to mine this type of data in order to discover regular specific patterns. In this article, we present a method for mining sequential patterns from multidimensional databases, at the same time taking advantage of the different dimensions and levels of granularity, which is original compared to existing work. The necessary definitions and algorithms are extended from regular sequential patterns to this particular case. Experiments are reported, showing the significance of this approach

show abstract

“…several characteristics of data can be ordered over time. Pinto et al [8], Zhang et al [17] and Yu et al [14] introduced the notion of multidimensionality in a sequence and proposed several algorithms to mine this type of data. In data warehouse environments, a background knowledge is usually available in form of taxonomies, classification or concept hierarchies.…”

Section: Introductionmentioning

confidence: 99%

A contribution to the discovery of multidimensional patterns in healthcare trajectories

et al. 2014

View full text Add to dashboard Cite

Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing dimensional items. However, in real-world scenarios, data sequences are described as combination of both multidimensional items and itemsets. These heterogeneous descriptions cannot be handled by traditional approaches. In this paper we propose a new approach called MMISP (Mining Multidimensional Itemset Sequential Patterns) to extract patterns from complex sequential database including both multidimensional items and itemsets. The novelties of the proposal lies in: (i) the way in which the data are efficiently compressed; (ii) the ability to reuse and adopt sequential pattern mining algorithms and (iii) the extraction of new kind of patterns. We introduce a case-study on real-world data from a regional healthcare system and we point out the usefulness of the extracted patterns. Additional experiments on synthetic data highlights the efficiency and scalability of the approach MMISP.

show abstract

ApproxMGMSP: A Scalable Method of Mining Approximate Multidimensional Sequential Patterns on Distributed System

Cited by 6 publications

References 7 publications

Distributed Sequential Pattern Mining: A Survey and Future Scope

Distributed Sequential Pattern Mining: A Survey and Future Scope

Mining multidimensional and multilevel sequential patterns

A contribution to the discovery of multidimensional patterns in healthcare trajectories

Contact Info

Product

Resources

About