Tatu Lund

Classification of Enterobacteriaceae by minimization of stochastic complexity

A new method for classifying bacteria is presented and applied to a large set of biochemical data for the Enterobacteriaceae. The method minimizes the bits needed to encode the classes and the items or, equivalently, maximizes the information content of the classification. The resulting taxonomy of Enterobacteriaceae corresponds well to the general structure of earlier classifications. Minimization of stochastic complexity can be considered as a useful tool to create bacterial classifications that are optimal from the point of view of information theory.

Stochastic complexity as a taxonomic tool

In this paper we propose a method of constructing a hierarchical classification based on the notion of stochastic complexity. Minimization of stochastic complexity amounts to maximization of the information content of the classification. A dendrogram is obtained by first finding the classification which minimizes stochastic complexity and then by step-wise merging of groups such that at each step there is a minimum loss of information. The method was applied to a database containing 5313 strains of Enterobacteriaceae. The results are in reasonable accordance with present-day views on the taxonomy of Enterobacteriaceae.

Minimizing stochastic complexity using local search and GLA with applications to classification of bacteria

In this paper, we compare the performance of two iterative clustering methods when applied to an extensive data set describing strains of the bacterial family Enterobacteriaceae. In both methods, the classification (i.e. the number of classes and the partitioning) is determined by minimizing stochastic complexity. The first method performs the minimization by repeated application of the generalized Lloyd algorithm (GLA). The second method uses an optimization technique known as local search (LS). The method modifies the current solution by making global changes to the class structure and it, then, performs local fine-tuning to find a local optimum. It is observed that if we fix the number of classes, the LS finds a classification with a lower stochastic complexity value than GLA. In addition, the variance of the solutions is much smaller for the LS due to its more systematic method of searching. Overall, the two algorithms produce similar classifications but they merge certain natural classes with microbiological relevance in different ways.

Enterobacteriaceae Taxonomy Approached by Minimization of Stochastic Complexity

We discuss the taxonomy of Enterobacteriaceae in the light of classification by minimization of stochastic complexity (SC). A classification which minimizes SC is optimal from the point of view of information theory. It was found that the SC-minimizing classification of a large database of strains of Enterobacteriaceae resulted in structures which correspond well to the conclusions of experts on the taxonomy of Enterobacteriaceae. The approach based on minimization of SC can therefore be considered as useful in bacterial taxonomy.

New Methods for the Analysis of Binarized BIOLOG GN Data of Vibrio species: Minimization of Stochastic Complexity and Cumulative Classification

We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.

An Assessment of Cumulative Classification

We present a method for building systematics when new knowledge is continuously accumulated. The resulting classification is self-correcting and improves itself by sorting new items as they are added to the material and studied. The formulation is based on Bayesian predictive probability distributions. A new item that has not yet been classified is assigned to the class that has maximal posterior probability or is made to form a group of its own. Such a cumulative classification depends on the order in which the items are classified. The introduction of an already classified training set considerably improves the repeatability of the method. As a case study we applied the method to a large data set for the Enterobacteriaceae. The resulting classifications corresponded well to the general structure of the prevailing taxonomy of Enterobacteriaceae.

Clustering by Adaptive Local Search with Multiple Search Operators

Abstract:Local Search (LS) has proven to be an efficient optimisation technique in clustering applications and in the minimisation of stochastic complexity of a data set. In the present paper, we propose two ways of organising LS in these contexts, the Multi-operator Local Search (MOLS) and the Adaptive Multi-Operator Local Search (AMOLS), and compare their performance to single operator (random swap) LS method and repeated GLA (Generalised Lloyd Algorithm). Both of the proposed methods use several different LS operators to solve the problem. MOLS applies the operators cyclically in the same order, whereas AMOLS adapts itself to favour the operators which manage to improve the result more frequently. We use a large database of binary vectors representing strains of bacteria belonging to the family Enterobacteriaceae and a binary image as our test materials. The new techniques turn out to be very promising in these tests.

On self-adaptation in multioperator local search

Local searching (LS) has proven to be an efficient optimization technique in clustering applications when minimizing stochastic complexity. In this paper, we propose a method for organizing LS in this context - the adaptive multi-operator local search (AMOLS) - and compare its performance to the non-adaptive multi-operator LS (MOLS) method. Both of these methods use several different LS operators to solve problems. MOLS applies the operators randomly, whereas AMOLS adapts itself to favour those operators which manage to improve the results more frequently. We use a large database of binary vectors representing strains of bacteria belonging to the family Enterobacteriaceae and a binary image as our test materials. The results show the benefits of self-adaptation.