Mixed data stream clustering method based on merging and pruning

Abstract

The invention discloses a mixed data stream clustering method based on merging and pruning, which comprises the following steps: converting a classification attribute value into a numerical attribute by using an important measurement criterion, normalizing data, and then reducing the dimension of the data by using a principal component analysis method. The mixed data stream clustering method adopts an online / offline two-stage processing framework. In the online stage, a new micro-cluster feature vector is adopted as a data structure to store data flow summary information, the data flow summary information required in the offline stage is dynamically maintained through a micro-cluster merging algorithm and a micro-cluster pruning algorithm, and the evolution process of the data flow is accurately reflected. In the offline stage, a density peak clustering method is adopted, the micro-clusters are used as virtual objects for clustering, and a final clustering result is obtained.

Description

technical field [0001] The invention relates to the technical fields of data stream mining and cluster analysis, in particular to a clustering method for mixed data streams based on merging and pruning. Background technique [0002] In today's era, people generate a variety of data streams through the use of the Internet. Data streams often have the characteristics of infinite, continuous, fast arrival, concept drift, etc. These characteristics make data stream mining face great challenges. In practical applications, the data that needs to be analyzed is often unlabeled, and the cost of obtaining data stream class labels is very high. Therefore, data stream clustering, as an unsupervised learning algorithm, has attracted extensive attention from researchers and has become an important topic in this context. a research hotspot. [0003] So far, most studies on data stream clustering have only focused on numerical data or categorical data, but not both. Mixed data streams a...

AI Technical Summary

Problems solved by technology

In some pruning algorithms that have been proposed, the pruning time depends on the value of the input parameters, resulting in high computational cost and memory usage

[0005] To sum up, there is an urgent need for an efficient hybrid data stream clustering method to reduce the computational cost and memory cost of hybrid data stream clustering. There are some problems in the current data stream clustering: (1) Traditional data stream clustering Most studies only focus on numerical data or categorical data, not both, which restricts the application of data stream clustering

(2) The maintenance of micro-clusters in the online stage of data stream clustering costs a lot of time and space.

Images