A theoretical framework is introduced to model data mining problems as the answering of queries in inductive databases. Inductive queries are requests to find out patterns in a database satisfying certain user-specified constraints. Through the analysis of the answer sets to inductive queries composed from anti-monotonic and monotonic basic predicates using Boolean operators, interesting proper...

Our main purpose in this dissertation is to develop a system to diagnose and indicate solutions to financial health problems of small and medium firms (SMF). Although monitoring and adjusting financial problems play a central role in the firm’s performance, usually SMF face difficulties in these tasks for lacking human resources and for having incapacity to afford a consultant. The closest type...

Scoring models yield continuous predictions instead of sharp classifications. Scoring customers for profitability, loyalty, or product affinity corresponds to an inductive fuzzy classification: The model represents a continuous membership function mapping the set of customers into the fuzzy set of interesting customers – the fuzzy target group. This chapter presents a method for membership func...

We explored people's inductive biases in category learning--that is, the factors that make learning category structures easy or hard--using iterated learning. This method uses the responses of one participant to train the next, simulating cultural transmission and converging on category structures that people find easy to learn. We applied this method to four different stimulus sets, varying in...

In this paper, we present coreset constructions for the fuzzy Kmeans problem. First, we show that one can construct a weak coresets for fuzzy K-means. Second, we show that there are coresets for fuzzy K-means with respect to balanced fuzzy K-means solutions. Third, we use these coresets to develop a randomized approximation algorithm whose runtime is polynomial in the number of the given points...

Nowadays, the available data become more and more voluminous and diverse by nature : vague data, missing data, numerical or symbolic data can be encountered. However, users are more interested in the knowledge which can be extracted from the data, than by the data themselves. Vis-à-vis the great quantity of available data, the effective processing of data is very cumbersome. In this thesis we a...

In this paper, we introduce a design of fuzzy neural networks based on scatter space for nonlinear modeling. To design the networks, we partition the input space in the scatter form using fuzzy c-means (FCM) clustering algorithm which generates the fuzzy rules in the premise part of the proposed networks. The partitioned spaces express the fuzzy rules of the networks. Through this method, we ar...

This paper proposes fuzzy multi-criteria decision-making approach integrated with fuzzy real option value theory. The applicability of the proposed method was shown on a real-world supermarket location selection problem. Based on the interviews with the experts, the evaluation criteria for retail location selection were identi ̄ed. Then the network for fuzzy analytic network process (ANP) method...

Worms spread by scanning for vulnerable hosts across the Internet. In this paper we report a comparative study of three classification schemes for automated portscan detection. These schemes include a simple Fuzzy Inference System (FIS) that uses classical inductive learning, a Neural Network that uses back propagation algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) that also emp...