Quality improvement is playing the key role in success of a business. Reduction variability main step for quality. Control charts are developed purpose monitoring quality characteristics with aim reducing variability. In many industries instead continuous variable categorical (ordinal) data used to measure interest. Hence developing control techniques ordinal has become recent research focus. p...

Learning distances from categorical attributes is a very useful data mining task that allows to perform distance-based techniques, such as clustering and classification by similarity. In this article we propose a new context-based similarity measure that learns distances between the values of a categorical attribute (DILCA DIstance Learning of Categorical Attributes). We couple our similarity m...

Although voting methods are a viable way to improve classification algorithm performance, these have usually been applied to complete training datasets. We propose a new voting methodology which is based on the success of each individual classifier as it is applied to particular classes in a training dataset. We test some specific variations on this theme and have found as much as a 12.8% impro...

the present study surveys the effect of bank-specific, industry-specific and macroeconomic conditions on bank performance to examine the structure-conduct-performance (scp) hypothesis in iranian banking industry. to achieve this goal, we drew upon the panel data technique to analyze the data of eleven public and private iranian banks during 2001-2006. firstly, productivity was calculated by usi...

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, bar charts. While other methods separate the creation of colors from their assignment, our takes data characteristics into account produce palettes, which are then assigned in a way that fosters better visual discrimination classes. To do so, we use cus...

Classification is an important research topic in knowledge discovery. Most of the researches on classification concern that a complete dataset is given as a training dataset and the test data contain all values of attributes without missing. Unfortunately, incomplete data usually exist in real-world applications. In this paper, we propose new handling schemes of learning classification models f...

algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art's evolution. To establish this, we use a categorical data set and we...