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Clustering method based on adjacent grid search

A clustering method and grid search technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems that cannot be solved by the rapid increase of algorithm complexity, the clusters cannot be effectively identified, and the algorithm cannot be effectively distinguished.

Pending Publication Date: 2020-02-11
TIANJIN CHENGJIAN UNIV
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Problems solved by technology

Through the above analysis, the current grid clustering algorithm has the following problems in grid processing: (1) Clusters with complex boundary shapes cannot be effectively identified; (2) The complexity of the algorithm increases rapidly with the dimension of the sample set (3) When multiple cluster boundaries are connected, the algorithm cannot effectively distinguish them, and tends to classify them into one cluster

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  • Clustering method based on adjacent grid search
  • Clustering method based on adjacent grid search
  • Clustering method based on adjacent grid search

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Embodiment Construction

[0033] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0034] Such as figure 1 Shown is a flowchart of a clustering method based on the adjacent grid search strategy of the present invention. Specifically include the following steps:

[0035] First, the original data is divided into grids, that is, the original data set is divided into a limited number of grid units (hereinafter referred to as cells) by using a multi-dimensional spatial grid, and it is assumed that the data points in the same cell belong to the same cluster; By setting up the three attributes of each cell's membership, density, and position, the statistical analysis of the cell is realized; the noise threshold is used to detect the grid, to determine whether there is noise data in the data set, and to perform necessary denoising processing.

[0036] The multidimensional space in this step is a d-dimensional spa...

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Abstract

The invention discloses a clustering method based on an adjacent grid search strategy, and the method comprises the steps: firstly carrying out grid division of original data: enabling an original data set to be divided into a limited number of cells through a multi-dimensional space grid, and carrying out the denoising processing when necessary; secondly, performing grid clustering on the divided data: processing the denoised grids by utilizing a halo threshold value, and dividing the denoised grids into halo cell elements and core cell elements; establishing an adjacent grid operator for quickly searching an adjacent cell element of one cell element; achieving a clustering process through two steps of core cell element clustering and halo cell element division, dividing all core cell elements into a plurality of class clusters through a traversal algorithm, and dividing halo cell elements into existing class clusters based on cell element distances; and finally, clustering optimization is carried out according to data characteristics and user requirements. Compared with the prior art, the method has the advantages that a new clustering method can be provided for rapidly increasing the dimension of the sample set, and class clusters with complex boundary shapes can be effectively identified.

Description

technical field [0001] The invention relates to the technical field of unsupervised pattern recognition and data mining, in particular to a grid-based clustering method. Background technique [0002] With the development of big data and network technology, there is a large amount of data surplus in various disciplines and fields, so cluster analysis has become an increasingly important technology. The process of dividing a collection of physical or abstract objects into classes of similar objects is called clustering. A cluster generated by clustering is a collection of data objects that are similar to objects in the same cluster and different from objects in other clusters. With the application of clustering in various fields, higher robustness requirements are put forward for clustering algorithms. The following specific datasets are of increasing interest in many applications: (1) noisy datasets; (2) large-scale datasets; (3) high-dimensional datasets; (4) datasets with...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/22
Inventor 李志猛王国锋赵坚黄钦
Owner TIANJIN CHENGJIAN UNIV
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