Application list clustering method and device and computing equipment

Abstract

The embodiment of the invention provides an application list clustering method and device and a piece of computing equipment. The method comprises the steps of obtaining application lists of a plurality of terminal equipment; for each application list, taking ordered application identifiers corresponding to the application lists as characteristic sets, and determining a fingerprint value corresponding to the characteristic set of each application list based on a Simhash algorithm; and classifying the application lists whose the fingerprint values meet clustering requirements as one category. According to the application list clustering method and device and the computing equipment provided by the invention, the purpose of clustering the application lists can be achieved with the relatively small data computing workload.

Description

technical field [0001] The invention relates to the technical field of information classification, in particular to an application list clustering method, device and computing equipment. Background technique [0002] With the popularization of smart operating systems such as Android (Android) and IOS, more and more applications are loaded on terminal devices (such as mobile phones, tablet computers, laptops, etc.). In order to organize the applications loaded on terminal devices, the application The concept of list came into being; the application list represents the collection of applications loaded on the terminal device; each application can have an application identification (such as application name, etc.) to distinguish other applications, and all applications loaded on the terminal device After the application identifiers are sorted, an ordered application list can be formed. [0003] For the purpose of counting the applications loaded on each terminal device, combin...

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Problems solved by technology

[0004] The inventors of the present invention have found through research that: the key point of clustering the collected application lists of massive terminal devices is to determine similar or even identical application lists from the collected application lists of massive terminal devices, and cluster the similar , and even the same application lists are classified into one category; however, due to the large number of terminal devices, and a terminal device corresponds to its own application list, this makes the process of clustering the application list, there is a large-scale data Calculation objects, and the data calculation process with a large workload; therefore, how to provide an application list clustering scheme with a small data calculation workload has become a problem that those skilled in the art need to consider

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