[1]LI Hailin,GUO Ren,WAN Xiaoji.A minimum distance measurement method for amultivariate time series based on the feature matrix[J].CAAI Transactions on Intelligent Systems,2015,10(3):442-447.[doi:10.3969/j.issn.1673-4785.201405047]
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A minimum distance measurement method for amultivariate time series based on the feature matrix
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
10
Number of periods:
2015 3
Page number:
442-447
Column:
学术论文—人工智能基础
Public date:
2015-06-25
- Title:
- A minimum distance measurement method for amultivariate time series based on the feature matrix
- Keywords:
- multivariate time series; similarity measurement; feature matrix; minimum distance; principal component analysis; Hungary algorithm; data mining
- CLC:
- TP301
- DOI:
- 10.3969/j.issn.1673-4785.201405047
- Abstract:
- Similarity measurement is one of the most important preliminary works in the process of multivariate data mining. Its quality directly influences the performance and result of the later tasks of data mining. The data of every multivariate time series in dataset can be analyzed by the principal component analysis. The feature matrices are extracted to construct the corresponding new orthogonal coordinate systems whose distance can be measured by cosine value of the angles between two axes. Meanwhile, the Hungary algorithm is applied to the minimum distance computation of the two coordinate systems. In this way, the minimum distance measurement method for the multivariate time series based on the feature matrix is achieved. The results of experiment demonstrated that the proposed method has better quality of similarity measurement than the traditional ones and improves the effects of data mining for the multivariate time series.
- References:
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Last Update:
2015-07-15