Application of similarity aggregation techniques to a population of paraplegic patients

SUMMARYAfter a short methodological presentation of similarity aggregation in automatic classification, we will present an application to computational linguistics. We will try to explain, from an existing dictionary of synonyms, how we have (a) defined what was, in our opinion, the meaning of the synonymous relation we wanted to reveal in (b) transformed the existing dictionary into a sequence of matrices of synonymy, (c) checked with an adapted algorithm (similarity aggregation technique) if the links appearing in the (d) tried to improve the synonymous relation, a new optimized dictionary, existing dictionary corresponded to our synonymy definition, in order to propose more accurate data facilitating the management of a new dictionary and providing a classification of synonyms according to a semic separate valuation.

Applied linguistics: Optimization of semantic relations by data aggregation techniques

Warnesson

1985

Classification of medullary lesions among paraplegic patients by the likelihood of the links method

Lerman

1986

Relative to the problem presented and treated by Parisot in an earlier issue of this journal, we present a new solution based on our approach of hierarchical classification. The methodology used, that we will describe in a concise way, allows us to organize either the set of the descriptive variables or the set of the individuals. Its extreme generality includes a set theoretic coding which respects faithfully the globality of the intrinsic nature of the descriptive variables. Very rich results are obtained in terms of classification trees reduced to their 'significant' levels. One of the most significant levels corresponds to the partition obtained by Parisot. The 'significant' nodes indicate the most relevant associations. The signification is conceived from the empirical distributionon the successive tree levelsof association coefficients between partition and 'preordonnance' (i.e. total preorder on the set of unordered object pairs).KEY WORDS Data analysis Hierarchical classification Validation and significance Qualitative ordinal data Medical data Neurology RECALL OF THE PROBLEMWe will take here the presentation of the problem, given by Parisot.'In order to study a possible link between the characteristics of the spinal medulla and the occurrence of a particular disease, we want to find (as a first step) a classification of medullary lesions among paraplegic patients. First, let us recall some definitions (reproduced, slightly modified, from reference 1, pp. 36-37). The spinal medullathe medullary segmentsThe spinal medulla, an organ which belongs to the centzal nervous system, is a voluminous fascicule of nerve fibres, below the rachidian bulb. It is situated inside the vertebral column which then normally protects it. At its top, the spinal medulla is connected to the intracranial nervous centres. Along its whole length, it is connected to thirty pairs of rachidian nerves. These rachidian nerves carry the following information between the central nervous system and the striated muscles (those involved in the voluntary movements):(i) sensory information (upward fibres) (ii) motor information (downward fibres).

Clusterwise aggregation of relations

Gaul

Schader

1988

SUMMARYIn this paper we handle the general problem of finding q( > 1) central relations on a set of objects which best fit the information contained in a finite number of given relations on that set. The proposed CAR (clusterwise aggregation of relations) algorithm allows one to consider the well-known situation of determining a single central relation as a special case ( q = 1) and takes into account the fact that the rcpresentation of appropriately selected subsets of relations by different central relations can provide additional insights into whether different clusters or segments of relations exist in the given set of relations. Two examples demonstrate the usefulness of the suggested approach.