Procedure • Ensure good natural light or illumination on the chart. It is important to ensure that the person has the best possible chance of seeing and reading the test chart as treatment decisions are made based on the results of VA testing. • If the test is done outdoors, the chart should be in bright light and the patient in the shade, with enough light to illuminate the patient’s face duri...

K-means algorithm has been shown to be an effective and efficient algorithm for clustering. However, the k-means algorithm is developed for numerical data only. It is not suitable for the clustering of non-numerical data. K-modes algorithm has been developed for clustering categorical objects by extending from the k-means algorithm. However, no one applies this technique for classification of c...

Several distance measures have been proposed for comparing sets of LPC coefficients. The most popular one has been the "log likelihood ratio" proposed by Itakura [1]. In this paper we discuss this measure (strictly speaking, a somewhat generalized version of it) from both a theoretical and a practical point of view. We derive its statistical properties both when the reference vector is known an...

A distance measure between objects is a key requirement for many data mining tasks like clustering, classification or outlier detection. However, for objects characterized by categorical attributes, defining meaningful distance measures is a challenging task since the values within such attributes have no inherent order, especially without additional domain knowledge. In this paper, we propose ...

The mean shift algorithm is a widely used non-parametric clustering algorithm. It has been extended to cluster a mixture of linear subspaces for solving problems in computer vision such as multibody motion segmentation, etc. Existing methods only work with a set of subspaces, which are computed from samples of observations. However, noises from observations can distort these subspace estimates ...

With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D point-set objects, by clustering noisy point-set images. Unlike traditional clustering methods which use distance measures that operate on feature vectors a representation common to most problem domains this object-based clustering technique employs a distance measure specific to ...

We compare vectors containing counts of trigrams of part-of-speech (POS) tags in order to obtain an aggregate measure of syntax difference. Since lexical syntactic categories reflect more abstract syntax as well, we argue that this procedure reflects more than just the basic syntactic categories. We tag the material automatically and analyze the frequency vectors for POS trigrams using a permut...

Interval data is attracting attention from the data analysis community due to its ability to describe complex concepts. Since clustering is an important data analysis tool, extending these techniques to interval data is important. Applying traditional clustering methods on interval data loses information inherited in this particular data type. This paper proposes a novel dissimilarity measure w...

Video is a unique multimedia data type, in that it comes with distinguished spatio-temporal constraints. Content-based video retrieval thus requires methods for video sequence-to-sequence matching, incorporating the temporal ordering inherent in a video sequence, without losing sight of the visual nature of the information in the sequence. Such methods will require reliable measures of similari...

In this paper, we extend the distance measure to the linguistic fuzzy sets, and develop the linguistic distance operators, such as linguistic weighted distance (LWD) operator, linguistic ordered weighted distance (LOWD) operator, and study some of their desired properties. These aggregation operators are very useful for decision-making problems because they establish a comparison between an ide...