Кузнецов Сергей Олегович

Gradual Discovery with Closure Structure of a Concept Lattice

2020 · CHAPTER · en

An approximate discovery of closed itemsets is usually based on either setting a frequency threshold or computing a sequence of projections. Both approaches, being incremental, do not provide any estimate of the size of the next output and do not ensure that “more interesting patterns” will be generated first. We propose to generate closed itemsets incrementally, w.r.t. the size of the smallest (cardinality-minimal or minimum) generators and show that this approach (i) exhibits anytime property, and (ii) first generates itemsets of better quality and then those of lower quality.

Adaptive Multi-model Approaches to Pattern Set Mining

2020 · CHAPTER · en

On pattern setups and pattern multistructures

2020 · ARTICLE · en

Order and lattice theory provides convenient mathematical tools for pattern mining, in particular for condensed irredundant representations of pattern spaces and their efficient generation. Formal Concept Analysis (FCA) offers a generic framework, called pattern structures, to formalize many types of patterns, such as itemsets, intervals, graphs, and sequence sets. Moreover, FCA provides generic algorithms to generate irredundantly all closed patterns, the only condition being that the pattern space is a meet-semilattice. This does not always hold, e.g. for sequential and graph patterns. Here, we discuss pattern setups consisting of descriptions making just a partial order. Such a framework can be too broad, causing several problems, so we propose a new model, dubbed pattern multistructures, lying between pattern setups and pattern structures, which relies on multilattices. Finally, we consider some techniques, namely completions, transforming pattern setups to pattern structures using sets/antichains of patterns.

Reduced vs. standard dose native E. coli-asparaginase therapy in childhood acute lymphoblastic leukemia: long-term results of the randomized trial Moscow–Berlin 2002

2019 · ARTICLE · en

Purpose Favorable outcomes were achieved for children with acute lymphoblastic leukemia (ALL) with the first Russian multicenter trial Moscow–Berlin (ALL-MB) 91. One major component of this regimen included a total of 18 doses of weekly intramuscular (IM) native Escherichia coli-derived asparaginase (E. coli-ASP) at 10000 U/m2 during three consolidation courses. ASP was initially available from Latvia, but had to be purchased from abroad at substantial costs after the collapse of Soviet Union. Therefore, the subsequent trial ALL-MB 2002 aimed at limiting costs to a reasonable extent and also at reducing toxicity by lowering the dose for standard risk (SR−) patients to 5000 U/m2 without jeopardizing efficacy. Methods Between April 2002 and November 2006, 774 SR patients were registered in 34 centers across Russia and Belarus, 688 of whom were randomized. In arm ASP-5000 (n = 334), patients received 5000 U/m2 and in arm ASP-10000 (n = 354) 10 000 U/m2 IM. Results Probabilities of disease-free survival, overall survival and cumulative incidence of relapse at 10 years were comparable: 79 ± 2%, 86 ± 2% and 17.4 ± 2.1% (ASP-5000) vs. 75 ± 2% and 82 ± 2%, and 17.9 ± 2.0% (ASP-10000), while death in complete remission was significantly lower in arm ASP-5000 (2.7% vs. 6.5%; p = 0.029). Conclusion Our findings suggest that weekly 5000 U/m2E. coli-ASP IM during consolidation therapy are equally effective, more cost-efficient and less toxic than 10000 U/m2 for SR patients with childhood ALL.

Numerical Pattern Mining Through Compression

2019 · CHAPTER · en

Pattern Mining (PM) has a prominent place in Data Science and finds its application in a wide range of domains. To avoid the exponential explosion of patterns different methods have been proposed. They are based on assumptions on interestingness and usually return very different pattern sets. In this paper, we propose to use a compression-based objective as a well-justified and robust interestingness measure. We define the description lengths for datasets and use the Minimum Description Length principle (MDL) to find patterns that ensure the best compression. Our experiments show that the application of MDL to numerical data provides a small and characteristic subset of patterns describing data in a compact way.

Increasing the efficiency of packet classifiers with closed descriptions

2019 · CHAPTER · en

Efficient representation of packet classifiers has become a significant challenge due to the rapid growth of data stored and processed in the forwarding, or routing, tables. In our work we propose two algorithms for reducing the size of forwarding tables both in length and width by the deletion of redundant bits and unreachable rules based on FCA analysis. We consider the task of transferring the forwarding packet to the correct destination as the task of multinomial classification. Thus, the process of reducing the forwarding table size corresponds to feature selection procedure with slight modifications. The presented techniques are based on closed descriptions and decision trees. The main challenge in applying decision trees to the task is processing the overlapping rules. To overcome this challenge we propose to employ concept-based hypotheses to delete unreachable actions assigned to the overlapping rules. The experiments were performed on data generated by the ClassBench software. The proposed approach results in significant decrease in bits in the forwarding tables as features.

On interestingness measures of formal concepts

2018 · ARTICLE · en

Formal concepts and closed itemsets proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing domain taxonomies and ontologies. Exponential explosion makes it difficult to consider the whole concept lattice arising from data, one needs to select most useful and interesting concepts. In this paper interestingness measures of concepts are considered and compared with respect to various aspects, such as efficiency of computation and applicability to noisy data and performing ranking correlation.

MDL for FCA: is there a place for background knowledge?

2018 · CHAPTER · en

Detecting logical argumentation in text via communicative discourse tree

2018 · ARTICLE · en

We solve the argument mining problem by investigating discourse and communicative text structure. A new formal graph-based structure called communicative discourse tree (CDT) is defined. It consists of a discourse tree with additional labels on edges, which stand for verbs. These verbs represent communicative actions. Discourse trees are based on rhetoric relations, extracted from a text according to Rhetoric Structure Theory. The problem is tackled as a binary classification task, where the positive class corresponds to texts with arguments and the negative class corresponds to texts with no arguments. The feature engineering for the classification task is conducted, deciding on which syntactic and discourse features are associated with logical argumentation. Text classification framework based on syntactic, discourse and communicative discourse text structures with a number of learning approaches is implemented. Evaluation on a combined data-set is provided.

Как улучшить оценку множеств признаков с помощью принципа минимальной длины описания?

2018 · CHAPTER · ru