Pattern mining based on local distribution

Pattern mining gains more and more attention due to its useful applications in many areas, such as machine learning, database, multimedia, biology, and so on. Though there exist a lot of approaches for pattern mining, few of them consider the local distribution of the data. In the paper, we not only design six challenge datasets related to the local patterns, but also propose a new pattern mining algorithm based on local distribution. Unlike traditional pattern mining algorithms, our new algorithm first creates a local distribution for each data point by a random approach. Then, the distribution curve of each data point is simulated by the sum of low frequency curves obtained by the wavelet approach. In the third step, the coefficients of these low frequency curves for each data point are clustered by the normalized cut approach. Finally, the patterns of the datasets are obtained by the new pattern mining algorithm. The experiments show that our new algorithm outperforms traditional unsupervised learning approaches, such as K-means, EM, spectral clustering algorithm (SCA), and so on, on these six new datasets.