Martin Atzmueller

SD-map: a fast algorithm for exhaustive subgroup discovery

In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or sampling-based methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.

Subgroup discovery

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before that it also reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup discovery approaches. In that context, we also discuss experiences and lessons learned and outline some of the future directions in order to show the advantages and benefits of subgroup discovery. WIREs Data Mining Knowl Discov 2015, 5:35–49. doi: 10.1002/widm.1144

Anatomy of a conference

This paper presents an anatomy of Hypertext 2011 -- focusing on the dynamic and static behavior of the participants. We consider data collected by the CONFERATOR system at the conference, and provide statistics concerning participants, presenters, session chairs, different communities, and according roles. Additionally, we perform an in-depth analysis of these actors during the conference concerning their communication and track visiting behavior.

VIKAMINE: open-source subgroup discovery, pattern mining, and analytics

This paper presents an overview on the VIKAMINE system for subgroup discovery, pattern mining and analytics. As of VIKAMINE version 2, it is implemented as rich-client platform (RCP) application, based on the Eclipse framework. This provides for a highly-configurable environment, and allows modular extensions using plugins. We present the system, briefly discuss exemplary plugins, and provide a sketch of successful applications.

Face-to-face contacts at a conference: dynamics of communities and roles

This paper focuses on the community analysis of conference participants using their face-to-face contacts, visited talks, and tracks in a social and ubiquitous conferencing scenario. We consider human face-to-face contacts and perform a dynamic analysis of the number of contacts and their lengths. On these dimensions, we specifically investigate user-interaction and community structure according to different special interest groups during a conference. Additionally, using the community information, we examine different roles and their characteristic elements. The analysis is grounded using real-world conference data capturing community information about participants and their face-to-face contacts. The analysis results indicate, that the face-to-face contacts show inherent community structure grounded using the special interest groups. Furthermore, we provide individual and community-level properties, traces of different behavioral patterns, and characteristic (role) profiles.

On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties

While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In this paper, we analyze influence factors for link prediction in human contact networks. Specifically, we consider the prediction of new links, and extend it to the analysis of recurring links. Furthermore, we consider the impact of stronger ties for the prediction. The results and insights of the analysis are a first step onto predictability applications for human contact networks.

Generic pattern trees for exhaustive exceptional model mining

Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.

Community assessment using evidence networks

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes an approach for (relative) community assessment. We introduce a set of so-called evidence networks which are capturing typical interactions in social network applications. Thus, we are able to apply a rich set of implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented approach applying user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.

A case-based approach for characterization and analysis of subgroup patterns

Abstract In general, cases capture knowledge and concrete experiences of specific situations. By exploiting case-based knowledge for characterizing a subgroup pattern, additional information about the subgroup objects can be provided. This paper proposes a case-based approach for characterizing and analyzing subgroup patterns: It presents techniques for retrieving characteristic factors and a set of corresponding cases for the inspection and analysis of a specific subgroup pattern. Then, the set of factors and cases are merged into prototypical cases for presentation to the user. Such an alternative view on the subgroup pattern provides important introspective information on the subgroup objects, that is, the cases covered by the subgroup description: Using drill-down techniques, the user can perform a detailed introspection of a subgroup pattern using prototypical pattern cases. Additionally, these enable a convenient retrieval of interesting (meta-)information associated with the respective subgroup objects.

Towards capturing social interactions with SDCF: an extensible framework for mobile sensing and ubiquitous data collection

Social media as well as mobile devices have woven themselves into everyday life, mediating various implicit and explicit social interactions. The analysis and modeling of the interaction data, including both physical and online social interactions is receiving increasing interest. A prerequisite is then given by effective approaches for data collection, covering both sensor data and social media artifacts. This paper describes the Sensor Data Collection Framework (SDCF), a compact, versatile and easily extensible open source framework for mobile sensing and ubiquitous data collection. It provides an overview on core concepts and architecture. Furthermore, we discuss first experiences and results of applying the framework in a collaborative workgroup context.