The architecture of emergent self-organizing maps to reduce projection errors

Abstract. There are mainly two types of Emergent Self-Organizing Maps (ESOM) grid structures in use: hexgrid (honeycomb like) and quadgrid (trellis like) maps. In addition to that, the shape of the maps may be square or rectangular. This work investigates the effects of these different map layouts. Hexgrids were found to have no convincing advantage over quadgrids. Rectangular maps, however, are distinctively superior to square maps. Most surprisingly, rectangular maps outperform square maps for isotropic data, i.e. data sets with no particular primary direction.

Analyzing domestic violence with topographic maps: a comparative study.

Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the recent years, several variations on the Self Organizing Maps (SOM) were introduced in the literature. In this paper, the toroidal Emergent SOM tool and the spherical SOM are used to analyze a text corpus consisting of police reports of all violent incidents that occurred during the first quarter of 2006 in the police region Amsterdam-Amstelland (The Netherlands). It is demonstrated that spherical topographic maps provide a powerful instrument for analyzing this dataset. In addition, the performance of the toroidal Emergent SOM is compared to that of the spherical SOM, and it turned out to be superior to that of an ordinary classifier, applied directly to the data.Topographic maps; Domestic violence; Knowledge discovery in databases; Emergent SOM; BLOSSOM;

How emergent self organizing maps can help counter domestic violence.

Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the past years, new types of Self Organizing Maps (SOM) were introduced in the literature, including the recent Emergent SOM. The ESOM is used to study a large set of police reports describing a whole range of violent incidents that occurred during the year 2007 in the police region Amsterdam-Amstelland (the Netherlands). It is demonstrated that it provides an exploratory search instrument for examining unstructured text in police reports. First, it is shown how the ESOM was used to discover a whole range of new features that better distinguish domestic from non-domestic violence cases. Then, it is demonstrated how this resulted in a significant improvement in classification accuracy. Finally, the ESOM is showcased as a powerful instrument for the domain expert interested in an indepth investigation of the nature and scope of domestic violence.

Concept discovery innovations in law enforcement: a perspective.

In the past decades, the amount of information available to law enforcement agencies has increased significantly. Most of this information is in textual form, however analyses have mainly focused on the structured data. In this paper, we give an overview of the concept discovery projects at the Amsterdam-Amstelland police where Formal Concept Analysis (FCA) is being used as text mining instrument. FCA is combined with statistical techniques such as Hidden Markov Models (HMM) and Emergent Self Organizing Maps (ESOM). The combination of this concept discovery and refinement technique with statistical techniques for analyzing high-dimensional data not only resulted in new insights but often in actual improvements of the investigation procedures.Formal concept analysis; Intelligence led policing; Knowledge discovery;

A case of using formal concept analysis in combination with emergent self organizing maps for detecting domestic violence.

In this paper, we propose a framework for iterative knowledge discovery from unstructured text using Formal Concept Analysis and Emergent Self Organizing Maps. We apply the framework to a real life case study using data from the Amsterdam-Amstelland police. The case zooms in on the problem of distilling concepts for domestic violence from the unstructured text in police reports. Our human-centered framework facilitates the exploration of the data and allows for an efficient incorporation of prior expert knowledge to steer the discovery process. This exploration resulted in the discovery of faulty case labellings, common classification errors made by police officers, confusing situations, missing values in police reports, etc. The framework was also used for iteratively expanding a domain-specific thesaurus. Furthermore, we showed how the presented method was used to develop a highly accurate and comprehensible classification model that automatically assigns a domestic or non-domestic violence label to police reports.Formal concept analysis; Emergent self organizing map; Text mining; Actionable knowledge discovery; Domestic violence;

Concept Relation Discovery and Innovation Enabling Technology (CORDIET)

Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and Hidden Markov Models (HMM) as main artifacts in the analysis process. The user can define temporal, text mining and compound attributes. The text mining attributes are used to analyze the unstructured text in documents, the temporal attributes use these document's timestamps for analysis. The compound attributes are XML rules based on text mining and temporal attributes. The user can cluster objects with object-cluster rules and can chop the data in pieces with segmentation rules. The artifacts are optimized for efficient data analysis; object labels in the FCA lattice and ESOM map contain an URL on which the user can click to open the selected document

Emergence in Self Organizing Feature Maps

This paper sheds some light on the differences between SOM and emergent SOM (ESOM). The discussion in philosophy and epistemology about Emergence is summarized in the form of postulates. The properties of SOM are compared to these postulates. SOM fulfill most of the postulates. The epistemological postulates regarding this issue are hard, if not impossible, to prove. An alternative postulate relying on semiotic concepts, called "semiotic irreducibility" is proposed here. This concept is applied to U-Matrix on SOM with many neurons. This leads to the definition of ESOM as SOM producing a nontrivial U-Matrix on which the terms "watershed" and "catchment basin" are meaningful and which are cluster conform. The usefulness of the approach is demonstrated with an ESOM clustering algorithm which exploits the emergent properties of such SOM. Results on synthetic data also in blind studies are convincing. The application of ESOM clustering for a real world problem let to an excellent solution