Research Article
Structure Identification-Based Clustering According to Density Consistency
Algorithm 1
The process of new clustering algorithm.
Input: Data set , in which each row is a pattern; parameters: , alpha, beta. | Output: Label vector , in which each element is cluster label. | Step (1): Find nearest neighbors for each data point according to | Euclidean distance. | Step (2): Find nearest neighbors for each data point according to | Definition 3.2. | Step (3): Construct filtering matrix based on (3.2) and (3.8). | Step (4): Filtering process , obtain elementary clusters, which are | the element with various kind of structure features. Top-down | process is included to identify connectedness and direction. | Step (5): Integrate elementary clusters with same structure features into | meaningful clusters according to (3.9). | Step (6): Top-down process to identify noisy data points and outliers. |
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