Name
Abstract | ||
|---|---|---|
We present a unifying view of temporal concepts and data models in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data. In particular we distinguish time point-based methods and interval-based methods as well as univariate and multivariate methods. The mining paradigms and the robustness of many proposed approaches are compared to aid the selection of the appropriate method for a given problem. For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. For univariate data and limited gaps suffix tree methods are more efficient. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points. For time interval data with precise start and end points the relations of Allen can be used to formulate patterns. The recently proposed Time Series Knowledge Representation is more robust on noisy data and offers an alternative semantic that avoids ambiguity and is more expressive. For both pattern languages efficient mining algorithms have been proposed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1294301.1294302 | SIGKDD Explorations |
Keywords | Field | DocType |
pattern languages efficient mining,univariate data,unsupervised pattern mining,time interval data,symbolic temporal data,time point,mining paradigm,noisy data,data model,multivariate data,time point-based method,partial order,knowledge representation,time series,temporal data,sequential pattern mining | Data mining,Data modeling,Knowledge representation and reasoning,Time point,Collaborative filtering,Computer science,Robustness (computer science),Temporal database,Artificial intelligence,Suffix tree,Univariate,Machine learning | Journal |
Volume | Issue | Citations |
9 | 1 | 34 |
PageRank | References | Authors |
1.34 | 89 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Mörchen | 1 | 372 | 17.94 |