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Data stream effective clustering method based on tuple uncertainty

A clustering method and uncertainty technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems that do not take into account the uncertainty of tuples, so as to improve the quality of clustering and fast processing speed Effect

Inactive Publication Date: 2012-08-22
ZHEJIANG GONGSHANG UNIVERSITY
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AI Technical Summary

Problems solved by technology

At present, there are some algorithms for uncertain data stream clustering, such as UMicro algorithm, etc., but these algorithms only consider the uncertainty of the attribute value of the data tuple, and do not consider the uncertainty of the existence of the tuple.

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  • Data stream effective clustering method based on tuple uncertainty
  • Data stream effective clustering method based on tuple uncertainty

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Embodiment Construction

[0032] The present invention will be further described below in conjunction with the drawings.

[0033] Reference figure 1 , An effective data stream clustering method based on tuple uncertainty, including the following steps:

[0034] 1) Initialization

[0035] Divide the memory into two areas: the main buffer BUF M And sub-buffer BUF V , Respectively store the microcluster information of the "normal" tuple and the "outlier" tuple. BUF M And BUF V Initialization is empty.

[0036] 2) Find the home cluster

[0037] For the data stream with uncertain existence of tuples S={ 1 , P 1 > , L, k , P k > , Each new tuple that arrives in L} Find the accepted clusters in the main buffer and the sub buffer respectively.

[0038] First, in the main candidate area is a new tuple Finding the home cluster includes the following steps:

[0039] Step1: If the main buffer is not full, the number of microclusters in the candidate area is less than the size of the main buffer n m (|BUF M |m ), then the...

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Abstract

The invention relates to a data stream effective clustering method based on tuple uncertainty. The data stream effective clustering method comprises the following steps of: 1) initializing: dividing a memory into two regions, namely a main buffer area BUFM and a vice buffer area BUFV, respectively storing micro-cluster information of a normal tuple and an off-group tuple, and initializing the BUFM and the BUFV as empty; 2) seeking an attributive cluster: respectively seeking an acceptant cluster for each arrived new tuple in a data stream S which equals ((v1, p1), L, (vk, pk), L) with uncertainty in tuple existence from the main buffer area and the vice buffer area; and 3) updating and maintaining the micro-cluster information: step 1: performing attenuation operation; step 2: deleting micro-clusters which are too old; step 3: performing a cluster exchange mechanism; and step 4: filling in the main buffer area. The data stream effective clustering method disclosed by the invention considers the uncertainty in the tuple existence, improves the clustering quality, and has a very high processing speed.

Description

Technical field [0001] The present invention relates to information data processing technology, in particular to a clustering method for uncertain data streams. Background technique [0002] Clustering is the process of dividing data objects into classes or clusters, so that objects in the same cluster are "close" or related to each other, while objects in different clusters are "far away" or different. Clustering in data mining can be applied to many fields such as market research, pattern recognition, data analysis and image processing. As a large agricultural country, China accumulates a large amount of agricultural data including crop seedling conditions, soil conditions, water conditions, insect conditions, weather and disasters every year. In agriculture, data mining can be used for agricultural environmental analysis, pest control, agricultural meteorology, agricultural expert systems, agricultural market information, and agricultural germplasm resources. [0003] Due to t...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 刘东升琚春华张鹏坤周怡陈庭贵王蓓王冰
Owner ZHEJIANG GONGSHANG UNIVERSITY
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