Eclat-based multivariate time series association rule mining method

A rule and matrix technology, applied in the field of association rule mining under large-scale data, can solve problems such as lack and unification

Inactive Publication Date: 2018-01-09
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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Problems solved by technology

At present, there is a lack of systematic research and unified and effective models

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  • Eclat-based multivariate time series association rule mining method
  • Eclat-based multivariate time series association rule mining method
  • Eclat-based multivariate time series association rule mining method

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Embodiment Construction

[0027] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0028] Due to the large amount of time series data and the characteristics of real-time generation, it is necessary to compress the data before mining association rules, that is, feature representation. The feature representation of time series is to extract the characteristics of the data and transform the dimensions of the data. This can achieve the effect of feature dimensionality reduction. At the same time, the data in the low-dimensional space can also retain the information of the original time series as much as possible.

[0029] First, the present invention studies the feature representation method of TEO. Analyzing the data characteristics of the time series, there are often different trends on both sides of the segmentation point, which is analogous to the change in the gray level of the edge of the image in image processing. At t...

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Abstract

The invention provides an Eclat-based multivariate time series association rule mining method. the method comprises the steps of 1, generating a perpendicular dataset; 2, generating a MINHASH matrix,wherein the MINHASH matrix needs a designated parameter k; 3, utilizing the MINHASH matrix for estimating a candidate item set in an original data set; 4, according to the minimum support, pruning thecandidate item set to obtain frequent item sets 1; 5, combining two Hash frequent item sets 1 and generating a new frequent item set 2; 6, repeatedly executing the step 5 till combination cannot be performed, and ending an algorithm. The association rule mining speed is remarkably increased, the purpose of obtaining the time series data analysis result in time is achieved, even though the miningprecision is lowered, the mining efficiency can be greatly improved, and the machine memory can be saved.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a method for mining association rules under large-scale data. Background technique [0002] At present, there are some studies on approximate association rule mining at home and abroad. Because of their different research emphases, they use different association rule mining algorithms, and the characteristics of the mined association rules are also different. The steps of general approximate association rule mining are divided into two stages, first preprocessing operation, compression, smoothing, denoising, linearization approximation, time series segmentation, clustering, etc. on massive raw data, and then after processing The implementation of the approximate association rule mining algorithm is carried out on the processed data set. [0003] Traditional association rule mining algorithms are aimed at discrete data, and the mined association rules cannot refle...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/2255G06F16/2477G06N5/025
Inventor 张春慨
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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