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A k-dissimilarity D on a finite set X, | X| ≥ k, is a map from the set of size k subsets of X to the real numbers. Such maps naturally arise from edgeweighted trees T with leaf-set X: Given a subset Y of X of size k, D( Y ) is defined to be the total length of the smallest subtree of T with leaf-set Y . In case k = 2, it is well-known that 2-dissimilarities arising in this way can be characterized by the so-called '4-point condition'. However, in case k > 2 Pachter and Speyer () recently posed the following question: Given an arbitrary k-dissimilarity, how do we test whether this map comes from a tree? In this paper, we provide an answer to this question, showing that for k ≥ 3 a k-dissimilarity on a set X arises from a tree if and only if its restriction to every 2 k-element subset of X arises from some tree, and that 2 k is the least possible subset size to ensure that this is the case. As a corollary, we show that there exists a polynomial-time algorithm to determine when a k-dissimilarity arises from a tree. We also give a 6-point condition for determining when a 3-dissimilarity arises from a tree, that is similar to the aforementioned 4-point condition. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for classifying three-way dissimilarity data, which are reconstructed by a small number of consensus classifications of the objects each defined by a sum of two order constrained distance matrices, so as to identify both a partition and an indexed hierarchy. Specifically, the dissimilarity matrices are partitioned in homogeneous classes and, within each class, a partition and an indexed hierarchy are simultaneously fitted. The model proposed is mathematically formalized as a constrained mixed-integer quadratic problem to be fitted in the least-squares sense and an alternating least-squares algorithm is proposed which is computationally efficient. Two applications of the methodology are also described together with an extensive simulation to investigate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents asymmetric agglomerative hierarchical clustering algorithms in an extensive view point. First, we develop a new updating formula for these algorithms, proposing a general framework to incorporate many algorithms. Next we propose measures to evaluate the fit of asymmetric clustering results to data. Then we demonstrate numerical examples with real data, using the new updating formula and the indices of fit. Discussing empirical findings, through the demonstrative examples, we show new insights into the asymmetric clustering. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Classification is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Classification is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Classification is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper addresses the problem of specifying differential weights for variables in the construction of a measure of dissimilarity. An assessor is required to provide subjective judgments of the pairwise dissimilarities within a training set of objects, and these dissimilarities are then modeled as a function of the recorded differences between the objects on each of the variables. The aim is to make explicit the relative importance that assessors attach to each of the variables, and thus obtain guidance on how these variables should be combined into a relevant dissimilarity matrix. The methodology is illustrated by application to some archaeological data. [ABSTRACT FROM AUTHOR]