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We put forward a new conception, dynamic association rule, which can describe the regularities of changes over time in association rules. The dynamic association rule is different in that it contains not only a support and a confidence but also a support vector and a confidence vector. During the mining process, the data used for mining is divided into several parts according to certain time indicators,...
In the field of cluster analysis, most of existing algorithms are developed for small data sets, which cannot effectively process the large data sets encountered in data mining. Moreover, most clustering algorithms consider the contribution of each sample for classification uniformly. In fact, different samples should be of different contribution for clustering result. For this purpose, a novel typical-sample-weighted...
This paper proposes the problem of mining weighted generalized fuzzy association rules with fuzzy taxonomies (WGF-ARs). It is an extension of the generalized fuzzy association rules with fuzzy taxonomies problem. In order to reflect the importance of different items, the notion of generalized weights is introduced, and leaf-node items and ancestor items are assigned generalized weights in our WGF-ARs...
Different from familiar clustering objects, text documents have sparse data spaces. A common way of representing a document is as a bag of its component words, but the semantic relations between words are ignored. In this paper, we propose a novel document representation approach to strengthen the discriminative feature of document objects. We replace terms of documents with concepts in WordNet and...
Time series are comprehensively appeared and developed in many applications. Similarity search under time warping has attracted much interest between the time series in the large databases. DTW (Dynamic Time Warping) is a robust distance measure and is superior to Euclidean distance. Nevertheless, it is more unfortunate that DTW has a quadratic time and the false dismissals are come forth since DTW...
In this paper, an efficient and robust scene change detection algorithm is proposed by using low-level audiovisual features and several classification methods. The proposed algorithm consists of three stages. The first stage is shot boundary detection by using Support Vector Machine (SVM) and the second stage is the scene boundary detection using shot clustering based on visual information. In the...
Based on the FP-tree data structure, this paper presents an efficient algorithm for mining the complete set of positive correlated item pairs. Our experimental results on both synthetic and real world datasets show that, the performance of our algorithm is significantly better than that of the previously developed Taper algorithm over practical ranges of correlation threshold specifications.
As a simple, effective and nonparametric classification method, kNN algorithm is widely used in text classification. However, there is an obvious problem: when the density of training data is uneven it may decrease the precision of classification if we only consider the sequence of first k nearest neighbors but do not consider the differences of distances. To solve this problem, we adopt the theory...
Traditional information retrieval technologies can satisfy users’ needs to some extent. But they cannot satisfy any query from different backgrounds, with different intentions and at different time because of their all-purpose characteristics. An integrated searching algorithm by combining filtering with collaborative technologies is presented in this paper. The user model is represented as the probability...
As a novel research direction, privacy-preserving data mining (PPDM) has received a great deal of attentions from more and more researchers, and a large number of PPDM algorithms use randomization distortion techniques to mask the data for preserving the privacy of sensitive data. In reality, for PPDM in the data sets, which consist of terabytes or even petabytes of data, efficiency is a paramount...
We present a cost model for predicting the performance of R-tree and its variants. Optimization base on the cost model can be apply in R-tree construction. we construct a new R-tree variant called CR*-tree using this optimization technique. Experiments have been carried out ,results show that relative error of the cost model is around 12.6%,and the performance for querying CR*-tree has been improved...
In this paper, we will propose a novel outlier mining algorithm, called Grid-ODF, that takes into account both the local and global perspectives of outliers for effective detection. The notion ofOutlying Degree Factor(ODF), that reflects the factors of both the density and distance, is introduced to rank outliers. A grid structure partitioning the data space is employed to enable Grid-ODF to be implemented...
It is important to perform the clustering task on XML documents. However, it is difficult to select the appropriate parameters’ value for the clustering algorithms. Meanwhile, current clustering algorithms lack the effective mechanism to detect outliers while treating outliers as “noise”. By integrating outlier detection with clustering, the paper takes a new approach for analyzing the XML documents...
The projection-type neural networks $\tau \frac{dx}{dt}=-x+P_{\Omega }(x-\Lambda (t)\partial ^{0}E(x))$ are generic and useful models for solving the constrained optimization problems min {E(x)|x ∈ Ω}. In the existing convergence/ stability analysis, the most are deduced based on the assumptions that E is uniformly or strictly convex and Ω is box-shaped. In this talk we present a generalized...
In this paper, we proposed a novel hierarchical algorithm to recognize English calling cards. The algorithm processes multiresolution images of calling cards hierarchically to extract characters and recognize the characters by using an enhanced neural network method. Each processing step functions at lower overhead and results improved output. That is, first, horizontal smearing is applied to a 1/3...
Based on the concept of Immunodominance and Antibody Clonal Selection Theory, This paper proposes a new artificial immune system algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA), for multiobjective 0/1 knapsack problems. IDCMA divides the individual population into three sub-populations and adopts different evolution and selection strategies at them, but the update of each sub-population...
This paper proposes a rate-distortion (RD) based seeded region growing (SRG) for extracting an object such as breast tumors in ultrasound volumes which contain speckle noise and indistinct edges. In the proposed algorithm, region growing proceeds in such a way that the growing cost is minimized which is represented as the combination of rate measuring the roughness of a region contour and distortion...
Microarray technology has been widely used in biological and medical research to observe a large number of gene expressions. However, such experiments are usually carried out with few replica or instances, which may lead to poor modelling and analysis. This paper suggests an approach to improve classification by using synthetic data. A new algorithm is proposed to estimate synthetic data value and...
A method to estimate long distance navigation of a mobile robot is proposed. The method uses the dead reckoning,sonar and infrared sensors to detect the landmarks. A corridor environment with equal spaced convex edges is applied as the mobile robot’s moving space and the convex edges are used as landmarks for the robot mounted with the combined sensor system to estimate its position. The robot detects...
We present a novel algorithm SASAlignSimulated annealing with star-alignment. In the SASAlign, instead of starting with an initial solution chosen at random, we use the results formed by star-alignment to give a good starting point as the initial solution to the SA for further refinement. The time required by the algorithm scales linearly with the number of sequences in S, linearly with the number...
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