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Sequence data association rule mining method based on fragment clustering

A sequence data and clustering technology, applied in relational databases, database models, structured data retrieval, etc.

Pending Publication Date: 2021-04-30
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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  • Abstract
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Problems solved by technology

However, in the existing literature, there are very few literatures on fragmented clustering for sequence data and association rule mining based on this.

Method used

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  • Sequence data association rule mining method based on fragment clustering
  • Sequence data association rule mining method based on fragment clustering
  • Sequence data association rule mining method based on fragment clustering

Examples

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Embodiment Construction

[0032] The present invention is described in detail below in conjunction with accompanying drawing and embodiment, with figure 1 As an example, the technical solution for realizing the mining method of sequence data association rules based on segment clustering is as follows:

[0033] Step 1. Set the sliding window size w, the number of clusters clusterNum, the number of iterations num_iter, and the threshold mDtw;

[0034] In this embodiment, the data set used is air quality data, and six characteristics of PM2.5, PM10, NO2, CO, O3 and SO2 are selected to form multivariate sequence data, and the sliding window size w=5 is set, and the clustering Number clusterNum=9, number of iterations num_iter=100, threshold mDtw=0.1;

[0035] Step 2. According to w, use the sliding window algorithm to divide the original sequence data S into subsequence sets subS;

[0036] Step 3, each subsequence is normalized, and the normalization method is shown in formula (1),

[0037]

[0038] ...

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Abstract

The invention relates to a sequence data association rule mining method based on fragment clustering, and belongs to the field of data mining under the computer discipline. The method comprises the following steps: setting parameters; dividing the original sequence data into a sub-sequence set by using a sliding window algorithm, and normalizing each sub-sequence; clustering the sub-sequences by using a k-means algorithm, and calculating the distance between each sub-sequence and the central point by using a DTW algorithm in the clustering process; combining the clustering results, and forming an ordered transaction set T by the clustering results; generating a frequent item set based on the transaction set T, and generating an association rule; and screening and applying the association rules according to the confidence of each association rule.

Description

technical field [0001] The invention belongs to the field of data mining under the computer science, and in particular relates to a method for mining association rules between sequence fragments with the same variation trend in sequence data. Background technique [0002] The analysis of association rules can mine the relevant relations from a large number of transaction sets and reveal the potential association relations in the transaction sets. Association rule mining can discover interesting associations or interrelationships between item sets in a large amount of data, but generally speaking, it mines frequent item sets of transaction data itself. For time series forecasting, the method of regression forecasting is generally used to analyze the time series, and to find the law that changes with time in the series, but there is less mining for the correlation and association between subsequences. [0003] The trend of change in sequence data refers to the law of data ups...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F16/28G06K9/62
CPCG06F16/2465G06F16/285G06F18/23213
Inventor 陈红倩孙丽萍
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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