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The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC. [ABSTRACT FROM AUTHOR]

Nowadays information is crucial in many fields such as medicine, science and business, where databases are used effectively for information sharing. However, the databases face the risk of being pirated, stolen or misused, which may result in a lot of security threats concerning ownership rights, data tampering and privacy protection. Watermarking is utilized to enforce ownership rights on shared relational databases. Many reversible watermarking methods are proposed recently to protect rights of owners along with recovering original data. Most state-of-the-art methods modify the original data to a large extent, result in data quality degradation, and cannot achieve good balance between robustness against malicious attacks and data recovery. In this paper, we propose a robust and reversible database watermarking technique, Genetic Algorithm and Histogram Shifting Watermarking (GAHSW), for numerical relational database. The genetic algorithm is used to select the best secret key for grouping database, where the watermarking can be embedded with balanced distortion and capacity. The histogram of the prediction error is shifted to embed the watermark with good robustness. Experimental results demonstrate the effectiveness of GAHSW and show that it outperforms state-of-the-art approaches in terms of robustness against malicious attacks and preservation of data quality. [ABSTRACT FROM AUTHOR]

Advancement in information technology is playing an increasing role in the use of information systems comprising relational databases. These databases are used effectively in collaborative environments for information extraction; consequently, they are vulnerable to security threats concerning ownership rights and data tampering. Watermarking is advocated to enforce ownership rights over shared relational data and for providing a means for tackling data tampering. When ownership rights are enforced using watermarking, the underlying data undergoes certain modifications; as a result of which, the data quality gets compromised. Reversible watermarking is employed to ensure data quality along-with data recovery. However, such techniques are usually not robust against malicious attacks and do not provide any mechanism to selectively watermark a particular attribute by taking into account its role in knowledge discovery. Therefore, reversible watermarking is required that ensures; (i) watermark encoding and decoding by accounting for the role of all the features in knowledge discovery; and, (ii) original data recovery in the presence of active malicious attacks. In this paper, a robust and semi-blind reversible watermarking (RRW) technique for numerical relational data has been proposed that addresses the above objectives. Experimental studies prove the effectiveness of RRW against malicious attacks and show that the proposed technique outperforms existing ones. [ABSTRACT FROM PUBLISHER]

The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic networks that introduces the concept of temporal smoothness inside the community structure detection method. Evolutionary-based clustering approaches try to maximize cluster accuracy with respect to incoming data of the current time step, and minimize clustering drift from one time step to the successive one. In order to optimize both these two competing objectives, an input parameter that controls the preference degree of a user towards either the snapshot quality or the temporal quality is needed. In this paper the detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed. The main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive. Experiments on synthetic data sets show the very good performance of the method when compared with state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

Instance coreference resolution is an essential problem in studying semantic web, and it is also critical for the implementation of web of data and future integration and application of semantic data. In this paper, we propose to use Memetic Algorithm (MA) to solve this instance coreference problem in a sequential stage, i.e., the instance-level matching is carried out with the result of schema-level matching. We first give the optimization model for schema-level matching and instance-level matching. Then, we, respectively, present profile similarity measures and the rough evaluation metrics with the assumption that the golden alignment for both schema-level matching and instance-level matching is one-to-one. Furthermore, we give the details of the MA. Finally, the experiments of comparing our approach with the state-of-the-art systems on OAEI benchmarks and real-world datasets are conducted and the results demonstrate that our approach is effective. [ABSTRACT FROM AUTHOR]

Community structure division is a crucial problem in the field of network data analysis. Algorithms based on Markov chains are easy to use and provide promising solutions for community detection. In a Markov chain-based algorithm (i.e., MCL), a flow distribution matrix and a transition matrix are used to describe stochastic flows and transition probabilities, respectively, on a network. The dynamic interaction process between stochastic flows and transition probabilities in MCLs is manifested through an iterative process of updating the abovementioned two matrices. As one of the key mechanisms of MCLs, such a dynamic process for increasing the inhomogeneity directly affects the accuracy and computational cost of MCL-based methods. Inspired by a kind of positive feedback interaction of a dendritic network of tube-like amoeba cell pseudopodia (named the Physarum foraging network), a Physarum-inspired relationship among vertices is proposed to enhance the transition probability in the dynamic process of MCL-based community detection algorithms. Specifically, the proposed hybrid community detection algorithm can adaptively search for a better combination of parameters based on a genetic algorithm. Some experiments are carried out on both static and dynamic networks. The results show that the unique Physarum inspired algorithm achieved better computational efficiency and detection performance than other algorithms. [ABSTRACT FROM AUTHOR]