Mahdi Shahbaba

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Estimating the true number of clusters for an unlabeled data set is one of the most important limitations in clustering. To solve this issue, many approaches with different assumptions have been proposed in the literature. X-means clustering is one of the proposed methods, which employs Bayesian Information Criterion (BIC) to approximate the correct number(More)
We propose a new statistical test denoted by signature testing (Sigtest) with the application in clustering and image classification. Sigtest relies on probabilistic validation of empirical distribution function of data.We implement Sigtest to estimate the number of clusters in hierarchical and partitional clustering. In addition, we propose a new adaptive(More)
This paper provides a new model verification approach for Gaussian Mixture Models (GMM) with application in partitional clustering. The proposed method relies on the statistics of the data and model and transforms them into a denser area. The transformed data and model have smaller variation compared to their original versions. Therefore, this data(More)
This paper provides a new unimodality test with application in hierarchical clustering methods. The proposed method denoted by signature test (Sigtest), transforms the data based on its statistics. The transformed data has much smaller variation compared to the original data and can be evaluated in a simple proposed unimodality test. Compared with the(More)
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