In this paper, an approach to unsupervised pattern classifiation is discussed. The classification scheme is based on an approximation of the probability densities of each class under the assumption that the input patterns are of a normal mixture. The proposed technique for identifying the mixture does not require prior information. The description of the mixture in terms of convexity allows to determine, from a totally unlabeled set of samples, the number of components and, for each of them, approximate values of the mean vector, the covariance matrix, and the a priori probability. Discriminant functions can then be constructed. Computer simulations show that the procedure yields decision rules whose performances remain close to the optimum Bayes minimum error-rate, while involving only a small amount of computation.

An Approximate Solution to Normal Mixture Identification with Application to Unsupervised Pattern Classification

The present paper discusses a nonsupervised multicategory problem in terms of nonparametric learning. An algorithm for seeking modes of an unknown multidimensional probability density function (pdf) is considered by employing a hypercubic window function. The convergence proof of the algorithm is also presented. The discriminant function for multicategory problems is constructed by using the estimates of the modes of the multimodal pdf. An application of the mode estimation algorithm to nonparametric signal detection is described. The analytical result shows that our machine nearly converges to the optimal machine without supervision.

Nonparametric Learning Without a Teacher Based on Mode Estimation

An efficient algorithm to find the MLE of prior probabilities of a mixture in pattern recognition

An algorithm for non-supervised sequential of signals

With rampant urbanization and industrialization, the quest to become a Smart City is in vogue. Elaborate designs, ideas, and solutions to existing problems are being devised. However, in this race, city planners often neglect the most critical factor related to one&#x2019;s existence on the Earth, i.e., Sustainability. Designing smart and sustainable cities is a challenge whose solutions are not easy. Also, developing a proposed smart city is a time-consuming process as it involves its foundations, analyzing the needs, latest technology, infrastructure, and topography. This paper aims to analyze the patterns followed by cities worldwide in the quest to make smart cities. This shall also show how newer cities can become innovative based on the work already done by the existing cities through the concept of Inheritance. In order to do this, a dataset comprising 102 cities is analyzed based on six metrics. A combination of the Principal Component Analysis and K-means Clustering technique is used to assist with the analysis of this unlabeled dataset. It is thereby shown that these 102 cities can be identified as belonging to 4 clusters and the cluster properties can then be inherited by any new city poised to become a future smart city. 

Analysis of Smart and Sustainable Cities through K-Means Clustering

In this correspondence, an approach to unsupervised pattern classifiers is discussed. The classifiers discussed here have the ability of obtaining the consistent estimates of unknown statistics of input patterns without knowing the a priori probability of each category's occurrence where the input patterns are of a mixture distribution. An analysis is made about their asymptotic behavior in order to show that the classifiers converge to the Bayes' minmum error classifier. Also, some results of a computer simulation on learning processes are shown.

An Approach to Unsupervised Learning Classification

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An Approach to Unsupervised Learning Classification