Interval-valued process data monitoring and controlling

Interval-valued process data monitoring and controlling

Statistical process control, a recognized technique for improving quality and productivity, has been widely employed throughout various industries. The conventional Shewhart control charts are applicable only when the collected sample data are real-valued data. For the purpose of controlling uncertain information when interval-valued data inevitably appear in the manufacturing or service proces...

Clustering Interval-valued Data Using an Overlapped Interval Divergence

As a common problem in data clustering applications, how to identify a suitable proximity measure between data instances is still an open problem. Especially when interval-valued data is becoming more and more popular, it is expected to have a suitable distance for intervals. Existing distance measures only consider the lower and upper bounds of intervals, but overlook the overlapped area betwe...

Symbolic Clustering with Interval-Valued Data

Multidimensional scaling of interval-valued dissimilarity data

Multidimensional scaling is a well-known technique for representing measurements of dissimilarity among objects as points in a p-dimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals. Each object is then no longer represented as point, but as a region of R p , i n s u c h a w ay that the minimum and maximum dis...

Constrained Regression for Interval-valued Data

Current regression models for interval-valued data do not guarantee that the predicted lower bound of the interval is always smaller than its upper bound. We propose a constrained regression model that preserves the natural order of the interval in all instances, either for in-sample fitted intervals or for interval forecasts. Within the framework of interval time series, we specify a general d...