Cluster analysis of a price index series based on the hierarchical division algorithm

doi:10.16511/j.cnki.qhdxxb.2015.21.010

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(1089 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract At present, e-commerce trade is not included in the consumer price index published by the National Bureau of Statistics of China. With the rapid development of e-commerce, the development of an online consumer price index(CPI) has become an urgent problem. Online transaction data supports real-time access and corresponds to actual transactions. Therefore, an online CPI should be more real-time and more accurate than the traditional CPI. However, the calculation of a classification price index requires classification of elementary price indexes, because there are differences in the classification standards used by different enterprises. This paper describes a hierarchical division algorithm for cluster analyses of price index series, which uses a correlation coefficient based distance and the Manhattan distance to measure the distances between price index series and then divides the series by two steps. The method uses ending conditions to stop the divisions, so that the cluster count need not be preset. Finally, the method is applied to practical cases with 219 of 226 price index series effectively divided, which indicates a good clustering result.

Keywords price index series divisive hierarchical clustering method correlation coefficient based distance Manhattan distance

ZTFLH:

TP391.4

Issue Date: 15 November 2015

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	CHU Hongyang
	CHAI Yueting
	LIU Yi

Cite this article:

CHU Hongyang,CHAI Yueting,LIU Yi. Cluster analysis of a price index series based on the hierarchical division algorithm[J]. Journal of Tsinghua University(Science and Technology), 2015, 55(11): 1178-1183.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2015.21.010 OR http://jst.tsinghuajournals.com/EN/Y2015/V55/I11/1178

[1] 陈娟, 余灼萍. 我国居民消费价格指数的短期预测[J]. 统计与决策, 2005, 2(2):40-41.CHEN Juan, Yu Zhuoping. Short-term forecasting of China consumer pricing index[J]. Statistics and Decision, 2005:2(2) 40-41.(in Chinese)
[2] Nordhaus WD. Quality changes in price indexes[J]. Journal of Economic Perspectives, 1998, 12(1):59-68.
[3] Koch B. E-Invoicing/EBilling:International market overview & forecast[R]. Deutsch:Billentis, 2014.
[4] Fu T. A review on time series data mining[J]. Engineering Application of Artificial Intelligence, 2011, 24(1):164-181.
[5] Plant C, Wohlschlager AM, Zherdin A. Interaction-based clustering of multivariate time series[C]//Ninth IEEE international conference on data mining. Miami, FL, USA:IEEE Press, 2009, 914-909.
[6] De Luca G, Zuccolotto P, A tail dependence-based dissimilarity measure for financial time series clustering[J]. Advances in Data Analysis and Classification, 2011(5):323-340.
[7] D'Urso P, Cappelli C, Di Lallo D, et al. Clustering of financial time series[J]. Physica A:Statistical Mechanics and its Applications, 2013, 392(9):2114-2129.
[8] Fong S. Using hierarchical time series clustering algorithm and wavelet classifier for biometric voice classification[J/OL].[2014-10-18]. http://www.hindawi.com/journals/bmri/2012/215019/.
[9] Ji M, Xie F, Ping Y. A dynamic fuzzy cluster algorithm for time series[J]. Abstract & Applied Analysis, 2013, 51(2):1781-1801.
[10] Duru O, Bulut E. A non-linear clustering method for fuzzy time series:Histogram damping partition under the optimized cluster paradox[J]. Applied Soft Computing, 2014, 24:742-748.
[11] Scotto M, Alonso A, Barbosa S. Clustering time series of sea levels:Extreme value approach[J]. Journal of Waterway, Port, Coastal, and Ocean Engineering, 2014, 136(4):215-225.
[12] Liao W. Clustering of time series data:A survey[J]. Pattern Recognization, 2005, 38:1857-1874.
[13] Han J, Kamber M, Pei J. 数据挖掘:概念与技术[M]. 3版. 范明, 孟晓峰, 译. 北京:机械工业出版社, 2012. Han J, Kamber M, Pei J. Data Mining:Concepts and Techniques[M]. 3rd ED. FAN Ming, MENG Xiaofeng, translate. Beijing:China Machine Press, 2012.(in Chinese)
[14] Rodrigues PP, Gama J, Pedroso JP. Hierarchical clustering of time series data streams[J]. IEEE Transaction on Knowledge and Data Engineering, 2008, 20(5):615-627.
[15] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies[C]//Proceedings of the first European conference on artificial life. Paris, France:MIT Press, 1991, 134-142.

[1]	NURMEMET Yolwas, LIU Junhua, WUSHOUR Silamu, REYIMAN Tursun, DAWEL Abilhayer. Crosslingual acoustic modeling in Uyghur speech recognition[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 342-346.
[2]	AISIKAER Rouzi, WANG Dong, LI Lantian, ZHENG Fang, ZHANG Xiaodong, JIN Panshi. Score domain speaking rate normalization for speaker recognition[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 337-341.
[3]	ZHENG Jun, LI Wenqing. Calibration of 3-D measurement system based on a double position sensitive detectors[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 411-416.
[4]	YANG Qianwen, SUN Fuchun. Remote sensing image recognition based on generalized regularized auto-encoders[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(2): 113-121.
[5]	YI Jiangyan, TAO Jianhua, LIU Bin, WEN Zhengqi. Transfer learning for acoustic modeling of noise robust speech recognition[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(1): 55-60.
[6]	TIAN Chuan, YE Xiaojun, WANG Zuliang, LI Xin. Multi-tag identification and collision avoidance with RFIDs in blood management[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(11): 1121-1126.
[7]	LU Wenhuan, QU Yuexin, YANG Yalong, WANG Jianrong, DANG Jianwu. Hybrid feature extraction from ultrasound images for a silent speech interface[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(11): 1159-1162,1169.
[8]	XIAO Xi, ZHOU Lu. Speech recognition adaptive clustering feature extraction algorithms based on the k-means algorithm and the normalized intra-class variance[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(8): 857-861.
[9]	LI Xiaofei, XU Qing, XIONG Hui, WANG Jianqiang, LI Keqiang. Cyclist detection based on detection proposals and deep convolutional neural networks[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(5): 491-496.
[10]	Aisikaer Rouzi, YIN Shi, ZHANG Zhiyong, WANG Dong, Askar Hamdulla, ZHENG Fang. THUYG-20: A free Uyghur speech database[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(2): 182-187.
[11]	YANG Yingchun, DENG Licai. Score regulation based on GMM token ratio similarity for speaker recognition[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 28-32.
[12]	NIE Ding, AN Xuehui. Mini-slump flow measurement tool based on self phase image processing[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(12): 1249-1254.
[13]	TIAN Yao, CAI Meng, HE Liang, LIU Jia. Speaker recognition system based on deep neural networks and bottleneck features[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(11): 1143-1148.
[14]	ZHENG Jun, WEI Haiyong. Three-dimensional object recognition and posture calculations based on the whitening transformation and curvature characteristics[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(10): 1025-1030.
[15]	CHEN Baohua, DENG Lei, DUAN Yueqi, CHEN Zhixiang, ZHOU Jie. Multiple model fusion in 3-D reconstruction: Illumination and scale invariance[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(9): 969-973.

Viewed

Full text

Abstract

Cited

Shared

Discussed