In this paper a new statistical measure for estimating the degree of dissimilarity between two symbolic objects whose features are multivalued symbolic data type is proposed. In addition two new simple representation techniques viz., interval type and magnitude type for the computed dissimilarity between the symbolic objects are introduced. The dissimilarity matrices obtained are not necessaril...

The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally ...

For complex data sets, the pairwise similarity or dissimilarity of data often serves as the interface of the application scenario to the machine learning tool. Hence, the final result of training is severely influenced by the choice of the dissimilarity measure. While dissimilarity measures for supervised settings can eventually be compared by the classification error, the situation is less cle...

Many real-world clustering problems are plagued by incomplete data characterized by missing or absent features for some or all of the data instances. Traditional clustering methods cannot be directly applied to such data without preprocessing by imputation or marginalization techniques. In this article, we put forth the concept of Penalized Dissimilarity Measures which estimate the actual dista...

The problem of application the dissimilarity measures for binary test sequences is investigated. Their relevance in generating controlled random tests substantiated. Dissimilarity measure AD ( T i , k ) between sets and considered, using characteristic distance D t i,j k,r which based on determination independent pairs identical data = belonging to two patterns . This allows us estimate degree ...

Very hard optimization problems, i.e., problems with a large number of variables and local minima, have been effectively attacked with algorithms which mix local searches with heuristic procedures in order to widely explore the search space. A Population Based Approach based on a Monotonic Basin Hopping optimization algorithm has turned out to be very effective for this kind of problems. In the...

A random forest (RF) predictor is an ensemble of individual tree predictors. As part of their construction, RF predictors naturally lead to a dissimilarity measure between the observations. One can also define an RF dissimilarity measure between unlabeled data: the idea is to construct an RF predictor that distinguishes the “observed” data from suitably generated synthetic data. The observed da...

• The Euclidean distance is usually considered inappropriate for species abundance data. We thus propose a new measure that unifies the and Bray-Curtis dissimilarity. A generalization of this index to measurement functional dissimilarity also proposed. Community ecologists consider explore multivariate structure This because may lead counterintuitive result which two sample plots with no in com...

A dissimilarity measure on a set of objects is Robinsonian if its matrix can be symmetrically permuted so that its elements do not decrease when moving away from the main diagonal along any row or column. The Robinson property of a dissimilarity reflects an order of the objects. If a dissimilarity is not observed directly, it must be obtained from the data. Given that an ordinal structure is as...