Certain data is a data whose values are known precisely whereas uncertain data means whose value are not known precisely. But data is always uncertain in real life applications. In data uncertainty attribute value is represented by a set of values. There are two types of attributes in data sets namely, numerical and categorical attributes. Data uncertainty can arise in both numerical and catego...

In this paper, twenty well known data mining classification methods are applied on ten UCI machine learning medical datasets and the performance of various classification methods are empirically compared while varying the number of categorical and numeric attributes, the types of attributes and the number of instances in datasets. In the performance study, Classification Accuracy (CA), Root Mea...

Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and...

We test for long-term damages from precipitation shocks in rural Brazil by observing wages of households that have permanently migrated from rural to urban areas. We find that large short-term precipitation shocks damage migrants’ long-term wages. We offer an analytical explanation for this outcome: credit-constrained households may be willing to accept lower wages in urban areas following the ...

In this paper we examine long-run house price convergence across US states using a novel econometric approach advocated by Pesaran (2007) and Pesaran et al. (2009). Our empirical modelling exercise employs a probabilistic test statistic for convergence based on the percentage of unit root rejections among all state house price differentials. Using a sieve bootstrap procedure, we construct confi...

OBJECTIVE To provide a context for classification in child psychiatry over last 45 years including debate over different approaches. METHOD The context for classification of child psychiatric disorders has changed drastically since the introduction of categorical classification and the multi-axial formulation in the Diagnostic and Statistical Manual (DSM) and the International Classification ...

In this paper the conventional fuzzy k-modes algorithm for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. To t...