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Visualization method for concept drift of data stream in dynamic data environment

A technology of concept drift and dynamic data, which is applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., and can solve the problem of blank visualization of concept drift

Inactive Publication Date: 2013-06-12
DALIAN UNIV OF TECH
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The only technologies related to the concept of data flow currently exist are mainly for real-time detection or classification of concept drift in data flow to provide support for follow-up work, but the visualization of concept drift is still blank in academia and industry

Method used

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  • Visualization method for concept drift of data stream in dynamic data environment
  • Visualization method for concept drift of data stream in dynamic data environment
  • Visualization method for concept drift of data stream in dynamic data environment

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Embodiment Construction

[0059] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0060] refer to figure 1 , the framework of a method for classifying data streams in a dynamic data environment in the present invention includes a data stream 101, a data stream collection module 102, a data stream division module 103, a kdq tree module 104, a concept detection module 105, a concept pool module 106, and a concept Statistical module 107, data flow concept transfer diagram module 108; includes the following steps:

[0061] Step 1: The data stream collection module 102 collects data from the data stream 101 in chronological order. The data flow 101 includes any type of data flow known to those skilled in the art, especially including network intrusion detection data flow, network security monitoring data flow, sensor data monitoring data flow and grid power supply data flow. Data streams are usually generated in real time, so computi...

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Abstract

The invention relates to the technical field of intelligent information processing, and discloses a visualization method for concept drift of a data stream in a dynamic data environment. The visualization method comprises the following steps: achieving static treatment on the data stream; establishing different concept presentation modes according to different concept drift modes and saving the different concept presentation modes in concept pools; and when a new data block comes, utilizing the KL divergence algorithm to search similar concept presentations in the concept pools, if the similar concept presentations exist, counting the similar concept presentations, and if not exist, adding the new data block into the concept pools to serve as a new concept to be saved. The visualization method can be used for detecting the changed drift types of various data streams, can fully analyze the concept drift process in the data stream through counting, finally, utilizes the Bays method to draw a concept drift and transition graph according to the statistic result, and visualizes the concept drift and transition graph for assisting data mining in a concept level.

Description

technical field [0001] The invention relates to the technical field of intelligent information processing, in particular to a data flow concept drift visualization method in a dynamic environment, which is applicable to network intrusion detection, network security monitoring, sensor data monitoring, power grid power supply and the like. Background technique [0002] With the in-depth development of information technology, traditional data mining methods are facing new challenges. The first thing that bears the brunt is the change of data form, from traditional static data to dynamic data flow form. Therefore, how to effectively mine data flow and extract internal The knowledge contained is gaining more and more attention from industry. [0003] Different from static data, data flow itself has three characteristics: mass, real-time and dynamic variability. These three characteristics require that the traditional data mining model must be adjusted and changed to adapt to the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 冯林姚远陈沣
Owner DALIAN UNIV OF TECH
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