The development of accurate models and efficient algorithms for the analysis of multivariate categorical data are important and longstanding problems in machine learning and computational statistics. In this paper, we focus on modeling categorical data using Latent Gaussian Models (LGMs). We propose a novel logistic stick-breaking likelihood function for categorical LGMs that can exploit recent...

Classification of categorical data always involves more complexities compared to the numerical data. Because, a firm outline cannot be drawn in case of categorical data. Different types of assumptions are followed by various researchers to treat such kind of data. Again, dissimilarity measures applied in case of numerical data cannot be applied directly in this case. In this paper, a new cluste...

Learning vector quantization (LVQ) is a supervised neural network method applicable in non-linear separation problems and widely used for data classification. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for classifying data with categorical values. The batch learning rules make possible to construct the learning methodology f...

The concept of similarity is fundamentally important in almost every scientific field. Clustering, distance-based outlier detection, classification, regression and search are major data mining techniques which compute the similarities between instances and hence the choice of a particular similarity measure can turn out to be a major cause of success or failure of the algorithm. The notion of s...

The authors provide a detailed empirical analysis of Canadian city housing prices. They examine the long-run relationship between city house prices in Canada from 1981 to 2005 as well as idiosyncratic relations between city prices and city-specific variables. The results suggest that city house prices are only weakly correlated in the long run, and that there is a disconnect between house price...

K-means algorithm has been shown to be an effective and efficient algorithm for clustering. However, the k-means algorithm is developed for numerical data only. It is not suitable for the clustering of non-numerical data. K-modes algorithm has been developed for clustering categorical objects by extending from the k-means algorithm. However, no one applies this technique for classification of c...

Machine learning is well developed amongst the scientific community in terms of theoretical foundations (statistics and algorithms) frameworks (Tensorflow, PyTorch, H2O). However, machine heavily focused on numerical data, or data mixed with some categorical data. For datasets, scientists engineers can enjoy reasonable success only a limited knowledge inner workings frameworks. it different sto...