FG Datenbank- und Informationssysteme
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Homogeneity of persons in a social network is based on the
similarity of their attributes. Traditional clustering algorithms like
hierarchical (agglomerative) clustering or DBSCAN take distances
between objects as input and find clusters of objects. Distance
functions need to satisfy the triangle inequality (TI) property, but
sometimes TI is violated and, in addition, not all attributes do have
the same influence on the network and thus may affect the network and
compromise the quality of resulting clusters. We present an adaptive
clustering-based quantitative weighting approach that is completely
embedded in logic. To facilitate the user interaction with the system,
we exploit the concept of relevance feedback. The approach takes user
feedback as input to improve the quality of clusters and finds
meaningful clusters where TI does not hold. In addition, it has the
capability of providing the user alternative possible feedbacks that
can be fulfilled. To test the approach, we evaluate a clustering
distance regarding an ideal solution. Experiments demonstrate the
benefits of our approach.
Traditional database query languages are based on set theory and crisp
first order logic. However, many applications require imprecise conditions which return result objects associated with a degree of fulfillment. For example, a research paper should be reviewed by giving a degree of fulfillment for originality and relevance. Very often, imprecise conditions of a query are of different importance.
Thus, a query language should allow the user to give conditions different weights.
This paper proposes a weighting approach which is realized by means of conjunction, disjunction, and negation. Thus, our weighting is completely embedded within a logic. As a result, logical rules are preserved and can be used for query reformulation and optimization. As underlying logic-based query language, we use the CQQL query language. Furthermore we demonstrate that our weighting approach is applicable to further logic-based query languages.
Whistleblower-Plattform
(2011)
Zivilitäre Forschung
(2011)