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

Citation data:

Qinghua Daxue Xuebao/Journal of Tsinghua University, ISSN: 1000-0054, Vol: 55, Issue: 11, Page: 1178-1183

Publication Year:
2015

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DOI:
10.16511/j.cnki.qhdxxb.2015.21.010
Author(s):
Hongyang Chu, Yueting Chai, Yi Liu
Tags:
Engineering, Computer Science, Mathematics
article description
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.

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