Categorical functional data represented by paths of a stochastic jump process with continuous time and finite set states are considered. As an extension the multiple correspondence analysis to infinite variables, optimal encodings over approximated using arbitrary basis functions. This allows dimension reduction, representation, visualisation in lower dimensional spaces. The methodology is impl...

Most of existing outlier detection methods assume that the factors (i.e., outlierness scoring measures) data entities (e.g., feature values and objects) are Independent Identically Distributed (IID). This assumption does not hold in real-world applications where different is dependent on each other and/or taken from probability distributions (non-IID). may lead to failure detecting important ou...

In geographic data analysis, one is often given point of different categories (such as facilities a university categorized by department). Drawing upon recent research on set visualization, we want to visualize category membership connecting points the same with visual links. Existing approaches that follow this path usually insist all members category, which may lead many crossings and clutter...

In this paper, a dynamic setting for data quality improvement is studied. such setting, there repeated search rules and fix of their violations until stability reached. The constraints considered here are simple constant edit searching done via association analysis. Repair relies on the set cover method. This paper contributes to field in three ways. First, it shown that with appropriate filter...

Overview: Categorical Data and Graphics Methods for discrete distributions – testing goodness of fit Hanging rootograms Robust distribution plots Methods for two-way frequency tables – understanding association Fourfold displays Sieve diagrams Mosaic displays and loglinear models for n-way tables Mosaic displays Mosaic matrices Correspondence analysis and MCA Logistic and logit regression Logit...

Let us consider the problem of sequential prediction of categorical data. Let D = {0, 1, . . . , d−1} denote the set of possible outcomes with d ≥ 2. Let x1, x2, . . . be an infinite sequence with values in D. Let Y1, Y2, . . . denote the sequence of predictions. This is a random sequence with values in D. Yn+1 predicts xn+1 and may depend on the first n outcomes x1, x2, . . . , xn, Y1, Y2, . ....

LVQ (Learning Vector Quantization) has been used to impute missing group membership and stratum weights in confirmatory factor analysis (CFA) model with continuous indicators (Chen, Tsai, & Yang, 2010; Tsai & Yang, 2012). Currently, categorical questionnaires (e.g., Binary and Likert-type items) are widely used in education, business, economy, and psychology tests as well as international large...