Hybrid filling method for incomplete data

Abstract

The invention discloses a hybrid filling method for incomplete data. The hybrid filling method comprises the following steps: (1) performing special value filling pre-processing on a missing data value in a data set; (2) extracting data attribute significant characteristics by utilizing a stack type automatic coding machine; (3) performing incremental clustering on the filled data set based on the extracted characteristics; (4) performing attribute value weighted filling on a data missing object by utilizing attribute values, corresponding to front k% objects which are most similar with the data missing object, in the obtained each clustering result; and judging difference between all missing data filling values of this time and a last filling value, and iteratively updating (2) to (4) until filling value convergence conditions are met. According to the embodiment of the invention, local similarity characteristics of data in the data set, the data clustering precision, in-class data filling accuracy and algorithm practical application non-supervision and timeliness are considered to construct an algorithm of firstly clustering the incomplete data and then filling the incomplete data, and the filling result precision and the filling algorithm speed are ensured through ideas of utilizing special value filling, adopting the stack type automatic coding machine, performing incremental clustering, performing weighted filing on in-class front k% complete data objects, and the like.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method for realizing mixed filling of incomplete data based on a stacked deep learning network and incremental clustering. Background technique [0002] With the development of the Internet of Things, social networks, and e-commerce, data has grown and accumulated at an unprecedented rate, and incomplete data has also followed, resulting in a serious reduction in data quality. In the actual data analysis process, the efficient filling preprocessing of missing data is another major problem facing the current academic and industrial circles. [0003] The earlier method uses the average value of the attribute value in the data set to fill in the missing data, and the other method directly deletes the records containing the missing value. Compared with directly deleting missing records, average filling produces more erroneous analysis results, but simple data deletion will serio...

AI Technical Summary

Problems solved by technology

However, no clustering can guarantee 100% correct division results, so selecting candidate filling data in the obtained clusters becomes the key

In addition, most of the clustering algorithms used in existing data filling need to specify the number o

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