Grid-ODF: Detecting Outliers Effectively and Efficiently in Large Multi-dimensional Databases
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 efficiently. Experimental results show that Grid-ODF outperforms existing outlier detection algorithms such as LOF and KNN-distance in terms of effectiveness and efficiency.

title: XAR-miner: efficient association rules mining for XML data
abstract: In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed Content Tree (IX-tree) or Multi-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Task-relevant concepts are generalized to produce generalized meta-patterns, based on which the large ARs that meet the support and confidence levels are generated.

title: PC-Filter: A Robust Filtering Technique for Duplicate Record Detection in Large Databases
abstract: In this paper, we will propose PC-Filter (PC stands for Partitio n Comparison), a robust data filter for approximately duplicate record detection in large databases. PC-Filter distinguishes itself from all of existing methods by using the notion of partition in duplicate detection. It first sorts the whole database and splits the sorted database into a number of record partitions. The Partition Comparison Graph (PCG) is then constructed by performing fast partition pruning. Finally, duplicate records are effectively detected by using internal and external partition comparison based on PCG. Four properties, used as heuristics, have been devised to achieve a remarkable efficiency of the filter based on triangle inequity of record similarity. PC-Filter is insensitive to the key used to sort the database, and can achieve a very good recall level that is comparable to that of the pair-wise record comparison method but only with a complexity of O(N 4/3). Equipping existing detection methods with PC-Filter, we are able to well solve the ”Key Selection” problem, the ”Scope Specification” problem and the ”Low Recall” problem that the existing methods suffer from.

title: Gene Specific Co-regulation Discovery: An Improved Approach
abstract: Discovering gene co-regulatory relationships is a new but important research problem in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest, called the target gene , identify its strongly co-regulated genes and the condition subsets where such strong gene co-regulations are observed. The study on this problem can contribute to a better understanding and characterization of the target gene. The existing method, using the genetic algorithm (GA), is slow due to its expensive fitness evaluation and long individual representation. In this paper, we propose an improved method for finding gene specific co-regulations. Compared with the current method, our method features a notably improved efficiency. We employ k NN Search Table to substantially speed up fitness evaluation in the GA. We also propose a more compact representation scheme for encoding individuals in the GA, which contributes to faster crossover and mutation operations. Experimental results with a real-life gene microarray data set demonstrate the improved efficiency of our technique compared with the current method.

title: Anomaly detection in high-dimensional network data streams: A case study
abstract: In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

title: A Color Indexing Scheme Using Two-level Clustering Processing for Effective and Efficient Image Retrieval
abstract: In this paper, we present a clustering-based color indexing scheme for effective amd efficient image retrieval, which is essentially an exploration on the application of clustering technique to image retrieval. In our approach, the color features are clustered automatically using a color clustering algorithm twice (called two-level clustering processing) and two color feature summarizations are obtained, e.g. Local Color Centriods (LCCs) and Global Color Centroids (GCCs). Based upon LCCs and GCCs, a three-level R-tree is bulit for indexing database images and performing effecitve and efficient image retrieval. The experiments show that this indexing scheme is effective and efficient in performing image retrieval.

title: Approximate treatment for calculation of the rate-distortion slope in EBCOT
abstract: Some novel approaches are presented aiming at reducing computation and memory for the EBCOT algorithm in JPEG2000. In the current structure of EBCOT, the algorithm for calculating the distortion reduction is still too complex for hardware-oriented design, and the memory space needed for the storage of large amounts of rate-distortion slopes is often unaffordable. In this paper, we propose a set of simplification schemes based on some approximate treatments by means of quantization median substitution and logarithm expression. These approaches can facilitate calculation of the rate-distortion slopes and reduce the memory by 50% (only 8 bits for each slope). Experimental results show that the quality of decoded images has only slightly decreased, which is negligible for most of the applications.

title: A Novel Approach of Face Detection based on Skin Color Segmentation and PCA
abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and principal components analysis(PCA) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which are darker than their neighborhoods to filter candidate faces farther; at the end, PCA method is used to extract the relevant information in human faces.

title: Enabling Safe Dynamic Component-Based Software Adaptation
abstract: Recomposable software enables a system to change its structure and behavior during execution, in response to a dynamic execution environment. This paper proposes an approach to ensure that such adaptations are safe with respect to system consistency. The proposed method takes into consideration dependency analysis for target components, specifically determining viable sequences of adaptive actions and those states in which an adaptive action may be applied safely. We demonstrate that the technique ensures safe adaptation (insertion, removal, and replacement of components) in response to changing external conditions in a wireless multicast video application.

title: Clustering in Dynamic Spatial Databases
abstract: Efficient clustering in dynamic spatial databases is currently an open problem with many potential applications. Most traditional spatial clustering algorithms are inadequate because they do not have an efficient support for incremental clustering.In this paper, we propose DClust, a novel clustering technique for dynamic spatial databases. DClust is able to provide multi-resolution view of the clusters, generate arbitrary shapes clusters in the presence of noise, generate clusters that are insensitive to ordering of input data and support incremental clustering efficiently. DClust utilizes the density criterion that captures arbitrary cluster shapes and sizes to select a number of representative points, and builds the Minimum Spanning Tree (MST) of these representative points, called R-MST. After the initial clustering, a summary of the cluster structure is built. This summary enables quick localization of the effect of data updates on the current set of clusters. Our experimental results show that DClust outperforms existing spatial clustering methods such as DBSCAN, C2P, DENCLUE, Incremental DBSCAN and BIRCH in terms of clustering time and accuracy of clusters found.

title: Performance study of Markov modulated fluid flow models with priority traffic
abstract: The steady-state performance of generic high-speed queuing system with priority traffic is studied, using the Markov modulated fluid models. the focus is on systems with traffic partitioned into two priorities according to a splitting factor alpha , so that low priority traffic can be discarded when congestion occurs. The results are used to further study issues of congestion control with priority traffic. In particular, the expected values, waiting times, variances of the buffer contents, and tail distributions are evaluated. It is found that the waiting time is more sensitive to changes of alpha than the buffer contents. This shows that for time-constrained traffic, the priority assignment is important for properly balancing the tradeoff between packet loss and delays. This model can be considered as a direct extension of the fluid-flow model to include priorities.<<ETX>>