Information management method and device

Abstract

The invention relates to an information management method and device. The invention provides a text clustering method. The method comprises the following steps: calculating the similarity between inputted search files of a preset quantity and files in a database; respectively clustering the files with the similarity to the search file in the database reaching a threshold value to obtain a first collection cluster; clustering collections under the same file in the first collection cluster to obtain a clustering result. By changing the traditional clustering concept, the quantity of initial clustering centers is not designated, the initial classification is not carried out, the clustering is completed by virtue of a search way, and the universality is higher; meanwhile, the clustering center does not need to be continuously adjusted in the clustering process, so that the expenditure is small, and the expandability is better.

Description

technical field [0001] The present disclosure relates to the field of video technology, in particular to a user text clustering method and device. Background technique [0002] Cluster analysis is one of the main tasks of data mining. The so-called data mining is usually related to computer science, through statistics, online analysis and processing, intelligence retrieval, machine learning, and pattern recognition and many other methods, from a large amount of data through algorithms to search for information hidden in it. [0003] At present, the commonly used clustering algorithm in the field of data mining is the K-MEANS algorithm. The K-MEANS algorithm randomly selects K documents from N documents as centroids, measures the distance from each remaining document to each centroid, and puts It classifies to the nearest centroid, and then recalculates the centroid of each class that has been obtained, and then repeats this process until the new centroid is equal to or smal...

AI Technical Summary

Problems solved by technology

[0004] However, in the K-means algorithm, the K value is used to describe the number of initial cluster centers, which is a pre-specified value and is usually difficult to estimate, so it is not possible to know in advance how many categories a given data set should be clustered into. most suitable

Secondly, in the K-means algorithm, it is necessary to determine an initial division according to the initial clustering center, and then optimize the initial division. Therefore, the selection of the initial clustering center has a greater impact on the clustering results. Once the initial value is selected Not good, may not be able to get effective clustering results

In addition, the K-MEANS algorithm needs to continuously adjust the sample classification and continuously calculate the adjusted new cluster centers. Therefore, when the amount of data is very large, the time overhead of the algorithm is very large.

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