Application of Evolutionary Algorithms to the Problem of New Clustering of Psychological Categories Using Real Clinical Data Sets Th. Villmannt, B. Villmannt and C. Albanit tInstitut filr Informatik Universit~t Leipzig D-04109 Leipzig, Augustusplatz 10/11, G e r m a n y (corresponding author) email:
[email protected] SKlinik und Poliklinik ffir Psychotherapie und psychosomatische Medizin Universit~t Leipzig, D-04107 Leipzig, Karl-Tauchnitz-Str. 25, G e r m a n y
Abstract
One of the mostly used method for acquisition of structures of interpersonal relationships in the area of psychodynamic psychotherapy research is the method of the 'Core Conflietual Relationship Theme' which allows a standardization of phrases in so-called standard categories. We reclustered these categories on the basis of a set of real clinical data by application of evolutionary algorithms which leads to an improvement of the clustering in comparison to earlier resulted cluster distribution. For the evolutionary algorithms we used a special (# * ~)-spring-off strategy balancing between the well known (#, A)- and (# + ),)-strategy. Furthermore, we developed a special migration scheme for handling the dynamic of subpopulations.
1
Introduction
Modern psychology uses all the standard methods of mathematical statistics to extract relevant features, structural informations and other data from several therapeutical approaches. One of the mostly used method for acquisition of structures of interpersonal relationships in the area of psycho-dynamic psychotherapy research is the method of the 'Core Conflictual Relationship Theme' (CCRT) developed by LUBORSKV , [Lub77, So193]. The method investigates short stories about relationships, so-called relationship-episodes, which are often reported by the patients in their therapeutical sessions [DTR+93]. In each of these episodes were the components wish of the subject, response of the object and response o.f the subject encoded which were used to perform the CCRT.
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BARBER ET AL. [BCCL90] determined a system of Smax ----34 so-called standard categories Sj to classify the wishes of patients in the episodes. Examples for such standard categories are: ' . . . I want to be accepted . . . ' , ' . . . I want to be u n d e r s t o o d . . . ' , ' . . . I want to be successful...', etc., i.e. the standard categories describes an aspect in verbal manner. Of course, these categories are often correlated in meaning because of a similarly describing psychological topic, i.e., it is obviously to see that there exists overlapping categories in the above sense. Therefore, they are collected in Cmax = 8 clusters Ck to reduce these correlations (BARBER ET AL., [BCCLg0]). Then in further work instead of the standard categories the clusters are used for the specification of the episode, whereby the clusters should have different meaning contents (in the sense of disjunction). The scheme of mapping the standard categories onto the respective clusters is also predefined in the above mentioned work [BCCL90] which, in general, leads to an improvement of the reliability of the C C R T - m e t h o d [LBS89, LCC90]. 1 The number and the interpretation of the clusters as well as the assignment of the standard categories are resulted from the experience of several number of psychotherapists using conventional statistic methods. However, as mentioned in [AVV+96] the clusters also are still correlated again. Furthermore, several considerations have shown that the used scheme of assignment leads still to low reliability rates for the C C R T method and misunderstanding in investigations based on this scheme. For further and more detailed critical remarks we refer to [AVV+96]. Yet, the correlation between the standard categories as well as between the clusters are difficult to capture, because of the non-measurable structure. Hence, the problem is now, how one can reform the clusters of standard categories in such a manner that the correlations will be reduced in a faithful way using the underlying meaning, additional therapeutical knowledge etc.. Furthermore we have to pay attention to the assumption that the set S of standard categories will not be changed during the reclustering and in addition t h a t the number of clusters is pretend. 2 In the present article we applied evolutionary algorithms to solve this clustering problem. For a better handling we transformed it in a partitioning one. The new clusters are compared with the original one and , second, with also new clusters obtained using the factor decomposition method.
2
Clustering by evolutionary algorithms according to a (p 9 A)-strategy
For solving the above described problem at first several psychotherapists judge (rate) a large number of therapy interviews of several patients. The raters determined the most relevant standard category Sj. of all wishes in the episodes 1Analog categories and clusters exist for the response of object and subjects, respectively. However, the here reported results are examplary and transformable to the other components. 2The last condition is necessary because of the compatibility to other approaches in psychodynamic psychotherapy research and methods which extend the CCRT-method.
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and, in addition, a second one Sj+, which have to be different from the first one but it is also well describing the considered episode (wish). All these pairs ., j+ , i = 1 . . . N ,
~-coefficient < 0.1
form a database P which implicitly contains
meaning no agreement
0.1 _< ~ < 0.4
weak agreement
0.4
