Conceptual knowledge discovery with frequent concept lattices

Knowledge discovery support environments include beside classical data analysis tools also data mining tools. For supporting both kinds of tools, a unified knowledge representation is needed. We show that concept lattices which are used as knowledge representation in Conceptual Information Systems can also be used for structuring the results of mining association rules. Vice versa, we use ideas of association rules for reducing the complexity of the visualization of Conceptual Information Systems.

CEM - visualization and discovery in Email

This paper presents a lattice-based visual metaphor for knowledge discovery in electronic mail. It allows a user to navigate email using a visual lattice metaphor rather than a tree structure. By using such a conceptual multi-hierarchy, the content and shape of the lattice can be varied to accommodate any number of queries against the email collection. The system provides more flexibility in retrieving stored emails and can be generalised to any electronic documents. The paper presents the underlying mathematical structures, and a number of examples of the lattice and multi-hierarchy working with a prototypical email collection.

Conceptual knowledge discovery and data analysis

In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin.

Normative and cognitive influences on female entrepreneurial reluctance at the base of the pyramid - an explorative study of cleaning ladies in Istanbul

Previous research has considered entrepreneurship as a way out of poverty and as a chance to foster economic growth. Moreover, specifically start-ups headed by women have played an important role in the economic development and it has been argued that gender-related issues, amongst others, play a significant role for the performance of a country or region. Against this background, this qualitative study explores desires, reluctances and constraints toward entrepreneurial activities of a comparably homogenous group of potential (poor) entrepreneurs in an emerging economy—cleaning ladies in Istanbul. We focus on this particular context as still rather little is known on reasons why women do not start a business (in Turkey). We believe exploring the reasons why certain individuals choose not to become entrepreneurs is at least as telling as investigating why they do so. We draw upon the social dimensions of entrepreneurship by Shapero and Sokol (1982) alongside Institutional Theory and posit that normative and cognitive forces may shape individual decisions on entrepreneurship. We identified two basic clusters of women and discuss possible hindrance factors undermining entrepreneurial desires and limitations for entrepreneurship as well as possible avenues for policy makers (and MNCs) to foster entrepreneurship in the given community.