In data mining and knowledge discovery, similarity between objects is one of the central concepts. A measure of similarity can be user-de ned, but an important problem is de ning similarity on the basis of data. In this thesis we consider three kinds of similarity notions: similarity between binary attributes, similarity between event sequences, and similarity between event types occurring in s...

An appropriate project delivery system (PDS) is crucial to the success of a construction projects. Case-based Reasoning (CBR) is a useful support for PDS selection. However, the traditional CBR approach represents cases as attribute-value vectors without taking relations among attributes into consideration, and could not calculate the similarity when the structures of cases are not strictly sam...

spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

Inspired by recent psychological findings on human similarity assessment, this paper introduces two methods to increase the classification accuracy and flexibility of similarity measures in CBR systems. Similarity measures are enhanced by imperfect domain theories. Attribute weights are inferred analytically from their relations to the classification goal, and additional virtual attributes are ...

One solution to process and analysis of massive graphs is summarization. Generating a high quality summary is the main challenge of graph summarization. In the aims of generating a summary with a better quality for a given attributed graph, both structural and attribute similarities must be considered. There are two measures named density and entropy to evaluate the quality of structural and at...

The problem to detect the similarity or the difference between objects are faced regularly in several domains of applications such as e-commerce, social network, expert system, data mining, decision support system, etc. This paper introduces a general model for measuring the similarity between objects based on their attributes. In this model, the similarity on each attribute is defined with dif...

Collaborative Filtering techniques offer recommendations to users by leveraging on the preferences of like-minded users. They thus rely highly on similarity measures to determine proximity between users. However, most of the previously proposed similarity measures are heuristics based and are not guaranteed to work well under all data environments. We propose a method employing Genetic algorith...

For high dimensional data, the redundant attributes of samplers will not only increase the complexity of the calculation, but also affect the accuracy of final result. The existing attribute reduction methods are encountering bottleneck problem of timeliness and spatiality. In order to looking for a relatively coarse attributes granularity of problem solving, this paper proposes an efficient at...

Generalized zero-shot learning (GZSL) is a technique to train deep model identify unseen classes using the attribute. In this paper, we put forth new GZSL that improves classification performance greatly. Key idea of proposed approach, henceforth referred as semantic feature extraction-based (SE-GZSL), use containing only attribute-related information in relationship between image and doing so,...