This paper proposes a subspace clustering algorithm by introducing attribute weights in the affinity propagation algorithm. A new step is introduced to the affinity propagation process to iteratively update the attribute weights based on the current partition of the data. The relative magnitude of the attribute weights can be used to identify the subspaces in which clusters are embedded. Experi...

The theory of concept lattice is an efficient tool for knowledge representation and knowledge discovery. One of the key problems of knowledge discovery is attribute reduction. This paper presents a novel approach to attribute reduction in formal concept lattices. The approach employs all the extents of the meet-irreducible elements in the lattice. Each of them determines a family of attribute s...

Facial attribute analysis in the real world scenario is very challenging mainly because of complex face variations. Existing works of analyzing face attributes are mostly based on the cropped and aligned face images. However, this result in the capability of attribute prediction heavily relies on the preprocessing of face detector. To address this problem, we present a novel jointly learned dee...

Modelling a target attribute by other attributes in the data is perhaps the most traditional data mining task. When there are many attributes in the data, one needs to know which of the attribute(s) are relevant for modelling the target, either as a group or the one feature that is most appropriate to select within the model construction process in progress. There are many approaches for select...

We provide a simple base algorithm for the given task of attribute selection as well as two improvements to this algorithm. We report on the results of their implementation and provide an error analysis. We then report some observations and conclusions about the human attribute selection process by comparing the output of our algorithms and the attributes selected by humans. Finally, we add som...

We present a method for automatically clustering similar attribute values in a database system spanning mulitple domains. The method constructs an aftribute abstraction hierarchy for each attribute using rules that are derived from the database instance. The rules have a confidence and popularity that combine to express the "usefullness" of the rule. Attribute values are clustered if they are u...

In a previous paper three types of missing attribute values: lost values, attributeconcept values and “do not care” conditions were compared using six data sets. Since previous experimental results were affected by large variances due to conducting experiments on different versions of a given data set, we conducted new experiments, using the same pattern of missing attribute values for all thre...

Generalized trapezoidal fuzzy numbers (GTFNs) have been widely applied in uncertain decision-making problems. The similarity between GTFNs plays an important part in solving such problems, while there are some limitations in existing similarity measure methods. Thus, based on the cosine similarity, a novel similarity measure of GTFNs is developed which is combined with the concepts of geometric...

Best practice dictates that security requirements be based on risk assessments; however, simplistic risk assessments that result in lists of risks or sets of scenarios do not provide sufficient information to prioritize requirements when faced with resource constraints (e.g., time, money). Multi-attribute risk assessments provide a convenient framework for systematically developing quantitative...