Abstract
This paper is concerned with the detection of false data, which is essential in data processing such as analysis of cellular proliferation of head and neck cancer. False data include, outliers and missing values. The characteristic of the method presented here is the use of computer graphics of an expert system in order to detect outliers. The determination whether measurement data are outliers or not is done by comparing the statistics with the expected value based on a chi-square test. Two examples are presented. One is normal data and the other is a case containing abnormal data.
We are going to develop an outlier detection supporting. system with an object-oriented approach. The suporting system presented here have various kinds of useful knowledge.
