Detecting Patterns of Fraudulent Behavior in Forensic Accounting

Often evidence from a single case does not reveal any suspicious patterns to aid investigations in forensic accounting and other forensic fields. In contrast, correlation of sets of evidence from several cases with suitable background knowledge may reveal suspicious patterns. Link Discovery (LD) has recently emerged as a promising new area for such tasks. Currently LD mostly relies on deterministic graphical techniques. Other relevant techniques are Bayesian probabilistic and causal networks. These techniques need further development to handle rare events. This paper combines first-order logic (FOL) and probabilistic semantic inference (PSI) to address this challenge. Previous research has shown this approach is computationally efficient and complete for statistically significant patterns. This paper shows that a modified method can be successful for discovering rare patterns. The method is illustrated with an example of discovery of suspicious patterns

Probabilistic Dynamic Logic of Phenomena and Cognition

The purpose of this paper is to develop further the main concepts of Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in the previous paper. The specific character of these logics is in matching vagueness or fuzziness of similarity measures to the uncertainty of models. These logics are based on the following fundamental notions: generality relation, uncertainty relation, simplicity relation, similarity maximization problem with empirical content and enhancement (learning) operator. We develop these notions in terms of logic and probability and developed a Probabilistic Dynamic Logic of Phenomena and Cognition (P-DL-PC) that relates to the scope of probabilistic models of brain. In our research the effectiveness of suggested formalization is demonstrated by approximation of the expert model of breast cancer diagnostic decisions. The P-DL-PC logic was previously successfully applied to solving many practical tasks and also for modelling of some cognitive processes.Comment: 6 pages, WCCI 2010 IEEE World Congress on Computational Intelligence July, 18-23, 2010 - CCIB, Barcelona, Spain, IJCNN, IEEE Catalog Number: CFP1OUS-DVD, ISBN: 978-1-4244-6917-8, pp. 3361-336

Visual Discovery in Multivariate Binary Data

This paper presents the concept of Monotone Boolean Function Visual Analytics (MBFVA) and its application to the medical domain. The medical application is concerned with discovering breast cancer diagnostic rules (i) interactively with a radiologist, (ii) analytically with data mining algorithms, and (iii) visually. The coordinated visualization of these rules opens an opportunity to coordinate the rules, and to come up with rules that are meaningful for the expert in the field, and are confirmed with the database. This paper shows how to represent and visualize binary multivariate data in 2-D and 3-D. This representation preserves the structural relations that exist in multivariate data. It creates a new opportunity to guide the visual discovery of unknown patterns in the data. In particular, the structural representation allows us to convert a complex border between the patterns in multidimensional space into visual 2-D and 3-D forms. This decreases the information overload on the user. The visualization shows not only the border between classes, but also shows a location of the case of interest relative to the border between the patterns. A user does not need to see the thousands of previous cases that have been used to build a border between the patterns. If the abnormal case is deeply inside in the abnormal area, far away from the border between normal and abnormal patterns, then this shows that this case is very abnormal and needs immediate attention. The paper concludes with the outline of the scaling of the algorithm for the large data sets

Prototype internet consultation system for radiologists

The overall purpose of this study is to develop a prototype radiological consultation system. We concentrate our work on prototype software environment for the system. The system provides a second diagnostic opinion based on similar cases, incorporating the experience of radiologists, their diagnostic rules and a database of previous cases. The system allows a radiologist to enter the description of a particular case using the lexicon such as BI-RADS of American College of Radiology and retrieve the second diagnostic opinion (probable diagnosis) for a given case. The system also allows a radiologist to get other important information too. These advances are based on a new computational intelligence technique and firstorder logic. We implemented a rule-based prototype diagnostic system. Two experimental Internet versions are currently available on the web and are under testing and evaluation of design. The diagnosis is based on the opinions of radiologists in combination with the statistically significant diagnostic rules extracted from the available database

Ontological Data Mining

We propose the ontological approach to Data Mining that is based on: (1) the analysis of subject domain ontology, (2) information in data that are interpretable in terms of ontology, and (3) interpretability of Data Mining methods and their results in ontology. Respectively concepts of Data Ontology and Data Mining Method Ontology are introduced. These concepts lead us to a new Data Mining approach—Ontological Data Mining (ODM). ODM uses the information extracted from data which is interpretable in the subject domain ontology instead of raw data. Next we present the theoretical and practical advantages of this approach and the Discovery system that implements this approach. The value of ODM is demonstrated by solutions of the tasks from the areas of financial forecasting, bioinformatics and medicine