5950644
Hiding Sensitive Association Rules with Limited Side Effects
Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries. The experimental results show the effectiveness of our approach, i.e., undesired side effects are avoided in the rule hiding process. The results also report that in most cases, all the sensitive rules are hidden without spurious rules falsely generated. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed
8794770
A Novel Representation of Sequence Data Based on Structural Information for Effective Music Retrieval
In this paper, we propose a novel representation of sequences based on the structural information of the sequences. A sequence is represented by a set of rules, which are derived from its subsequences. There are two types of subsequences of interest. One is called frequent pattern, which is a subsequence appearing often enough in the sequence. The other is called correlative pattern, which is a subsequence composed of highly correlated elements. The rules derived from the frequent patterns are called frequent rules, while the ones derived from the correlative patterns are called correlative rules. By considering music objects as sequences, we represent each of them as a set of rules and design a similarity function for effective music retrieval. The experimental results show that our approaches outperform the approaches based on the Markov-model on the average precision.
13332463
Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window
Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. In this paper, we develop a novel approach for mining frequent itemsets from data streams based on a time-sensitive sliding window model. Our approach consists of a storage structure that captures all possible frequent itemsets and a table providing approximate counts of the expired data items, whose size can be adjusted by the available storage space. Experiment results show that in our approach both the execution time and the storage space remain small under various parameter settings. In addition, our approach guarantees no false alarm or no false dismissal to the results yielded.
13420664
Music Classification Using Significant Repeating Patterns
With the popularity of multimedia applications, a large amount of music data has been accumulated on the Internet. Automatic classification of music data becomes a critical technique for providing an efficient and effective retrieval of music data. In this paper, we propose a new approach for classifying music data based on their contents. In this approach, we focus on monophonic music features represented as rhythmic and melodic sequences. Moreover, we use repeating patterns of music data to do music classification. For each pattern discovered from a group of music data, we employ a series of measurements to estimate its usefulness for classifying this group of music data. According to the patterns contained in a music piece, we determine which class it should be assigned to. We perform a series of experiments and the results show that our approach performs on average better than the approach based on the probability distribution of contextual information in music.
1673387
Two-Stage Optimal Component Analysis
Linear representations are widely used to reduce dimension in applications involving high dimensional data. While specialized procedures exist for certain optimality criteria, such as principle component analysis (PCA) and Fisher discriminant analysis (FDA), they can not be generalized for more general criteria. To overcome this fundamental limitation, optimal component analysis (OCA) uses a stochastic gradient optimization procedure intrinsic to the manifold giving by the constraints of applications and therefore gives a procedure for finding optimal representations for general criteria. However, due to its generality nature, OCA often requires extensive computation for gradient estimation and updating. To significantly reduce the required computation, in this paper, we propose a two-stage method by first reducing the dimension of input to a smaller one (but larger than the final resulting dimension) using a computationally efficient method and then performing OCA in the reduced space. This reduces the computation time from days to minutes on widely used databases, making OCA learning feasible for many applications. Additionally, since the reduced space is much smaller, the stochastic gradient optimization tends to be more efficient. We illustrate the effectiveness of the proposed method on face classification.
5502752
Natural Textures for Weather Data Visualization
In this paper we present a novel method to visualize weather data with multi-layer controllable texture synthesis. Texture possesses multiple principal perceptual channels, which makes it good at encoding multiple data attributes contained in weather data. The natural textures existed in the real world especially provide plenty of choices to encode the data with visually pleasing images. A controllable texture synthesis method is developed to generate a large amount of textures which change the appearances of their individual perceptual dimensions according to the underlying distribution of data attributes. In order to encode more data attributes we further propose multi-layer texture synthesis. The background and foreground textures are separately synthesized and then combined together for display. In the end, we apply our method to some real-world weather data and demonstrate its effectiveness with a user study
34177630
Genetic algorithm based methodology for breaking the steganalytic systems
Steganalytic techniques are used to detect whether an image contains a hidden message. By analyzing various image features between stego-images (the images containing hidden messages) and cover-images (the images containing no hidden messages), a steganalytic system is able to detect stego-images. In this paper, we present a new concept of developing a robust steganographic system by artificially counterfeiting statistic features instead of the traditional strategy by avoiding the change of statistic features. We apply genetic algorithm based methodology by adjusting gray values of a cover-image while creating the desired statistic features to generate the stego-images that can break the inspection of steganalytic systems. Experimental results show that our algorithm can not only pass the detection of current steganalytic systems, but also increase the capacity of the embedded message and enhance the peak signal-to-noise ratio of stego-images.
15058650
Query estimation by adaptive sampling
The ability to provide accurate and efficient result estimations of user queries is very important for the query optimizer in database systems. In this paper, we show that the traditional estimation techniques with data reduction points of view do not produce satisfiable estimation results if the query patterns are dynamically changing. We further show that to reduce query estimation error, instead of accurately capturing the data distribution, it is more effective to capture the user query patterns. In this paper, we propose query estimation techniques that can adapt to user query patterns for more accurate estimates of the size of selection or range queries over databases.
6210562
Discriminative training and explicit duration modeling for HMM-based automatic segmentation
HMM-based automatic segmentation has been popularly used for corpus construction for concatenative speech synthesis. Since the most important reasons for the inaccuracy of HMM-based automatic segmentation are the HMM training criterion and duration control, we will study these particular issues. For the HMM training, we apply the discriminative training method and introduce a new criterion, named Minimum SeGmentation Error (MSGE). In this method, a loss function directly related to the segmentation error is defined, and parameter optimization is performed by the Generalized Probabilistic Descent (GPD) algorithm. For the duration control problem, we apply explicit duration models and propose a two-step-based segmentation method to solve the problem of computational cost, where the duration model is incorporated in a postprocessor procedure. From the experimental results, these two techniques significantly improve segmentation accuracy with different focuses, where the MSGE-based discriminative training focuses on improving the accuracy of sensitive boundary, i.e., a boundary where an error in segmentation is likely to cause a noticeable degradation in speech synthesis quality, and the explicit duration modeling focuses on eliminating large errors. After combining these two techniques, the error average was reduced from 6.86 ms to 5.79 ms on Japanese data, and from 8.67 ms to 6.61 ms on Chinese data. Simultaneously, the number of errors larger than 30 ms were reduced 25% and 51% on Chinese and Japanese data, respectively.