Spatial Point-Data Reduction Using Pulse Coupled Neural Network

摘要

Many data mining algorithms are often found to be sensitive to the size of dataset. It may result in large memory requirements and very slow response time to execute tasks on large datasets. Thus, data reduction is an important issue in the field of data mining. This paper proposes a novel method for spatial point-data reduction. The main idea is to search a small subset of instances composed of border instances from original training set by using a modified pulse coupled neural network (PCNN) model. Original training instances are mapped into some pulse coupled neurons, and a firing algorithm is presented for determining which instances locate in border regions and filtering noisy instances. The reduced set maintains the main characteristics of original dataset and avoids the influence of noise, thus it can keep or even improve the quality of data mining results. The proposed method is a general data reduction algorithm, which can be used to improve classification, regression and clustering algorithms. The method achieves approximately linear time complexity, and can be used to process large spatial datasets. Experiments demonstrate that the proposed method is efficient and effective.