Detecting Projected Outliers in High-Dimensional Data Streams
In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective Genetic Algorithm (MOGA) is employed as an effective search method for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.

title: An Effective and Efficient Data Cleaning Technique in Large Databases

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: Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy
abstract: Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality. Recently, outlier detection for high-dimensional stream data became a new emerging research problem. A key observation that motivates this research is that outliers in high-dimensional data are projected outliers, i.e., they are embedded in lower-dimensional subspaces. Detecting projected outliers from high-dimensional stream data is a very challenging task for several reasons. First, detecting projected outliers is difficult even for high-dimensional static data. The exhaustive search for the outlying subspaces where projected outliers are embedded is a NP problem. Second, the algorithms for handling data streams are constrained to take only one pass to process the streaming data with the conditions of space limitation and time criticality. The currently existing methods for outlier detection are found to be ineffective for detecting projected outliers in high-dimensional data streams. 
In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensional data streams. SPOT employs an innovative window-based time model in capturing dynamic statistics from stream data, and a novel data structure containing a set of top sparse subspaces to detect projected outliers effectively. SPOT also employs a multi-objective genetic algorithm as an effective search method for finding the outlying subspaces where most projected outliers are embedded. The experimental results demonstrate that SPOT is efficient and effective in detecting projected outliers for high-dimensional data streams. The main contribution of this thesis is that it provides a backbone in tackling the challenging problem of outlier detection for high-dimensional data streams. SPOT can facilitate the discovery of useful abnormal patterns and can be potentially applied to a variety of high demand applications, such as for sensor network data monitoring, online transaction protection, etc.

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: Interweave Neural Networks with Evolutionary Algorithms, Cellular Computing, Bayesian Learning and Ensemble Learning
abstract: A neural networks are able to give solutions to complex problems in business intelligence and financial engineering due to their nonlinear processing. This paper consists of a survey of various business intelligence and financial engineering and so on applications based on the neural networks, and also a summary of the recent techniques such as still evolutionary algorithms, cellular computing, Bayesian learning and ensemble learning. The neural networks can be powerful tools in several fields of business intelligence and financial engineering. It can be concluded that, the future of neural networks lies in their use in conjunction with other advanced technologies.

title: Landmine Feature Extraction and Classification of GPR Data Based on SVM Method
abstract: In this paper, the problem of detecting buried landmine is tackled in the feature extraction and classification. Determining the likelihood set of an unknown pattern (feature vector), extracted from ground penetrating radar data by using SVM method. The advantage of SVM method in feature extraction and classification of image processing is: A classifier works well both on the training samples and on previously unseen samples; In addition, the SVM provides, enable a classification performance improvement based on from high feature dimensions to two or three feature dimensions. Finally, SVM method has a standard theory and a good implementation algorithm.

title: A New Heuristic Reduct Algorithm Base on Rough Sets Theory
abstract: Real world data sets usually have many features, which increases the complexity of data mining task. Feature selection, as a preprocessing step to the data mining, has been shown very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. To find the optimal feature subsets is the aim of feature selection. Rough sets theory provides a mathematical approach to find optimal feature subset, but this approach is time consuming. In this paper, we propose a novel heuristic algorithm based on rough sets theory to find out the feature subset. This algorithm employs appearing frequency of attribute as heuristic information. Experiment results show in most times our algorithm can find out optimal feature subset quickly and efficiently.

title: Numerical Simulation of Bag-Type Collector Flow Field in Biomass Energy Power Plant
abstract: With the computational fluid dynamics software FLUENT the flow field of flue gas from one 75 t/h circulating fluidized bed combustion boiler was simulated. The results showed that when air distribution was not taken into account, the flow of the two flue gases in the bag filter were not uniform. The flow field of coal flue gas was more disordered than that of biomass, but the majority of them moved upward directly. The uniformity was improved after the guide plate was added. Small particles and big ones of flue gas fell apart to two fractions in the middle of the guide plate, big ones on the left and small ones on the right, and both of them did circumferential motion in the bag filter. The results of the numerical simulation supplied reference to the operating and design of filtering bag-type collector for biomass plant.

title: Model-driven generative techniques for scalable performability analysis of distributed systems
abstract: The ever increasing societal demand for the timely availability of newer and feature-rich but highly dependable network-centric applications imposes the need for these applications to be constructed by the composition, assembly and deployment of off-the-shelf infrastructure and domain-specific services building blocks. Service oriented architecture (SOA) is an emerging paradigm to build applications in this manner by defining a choreography of loosely coupled building blocks. However, current research in SOA does not yet address the per for mobility (i.e., performance and dependability) challenges of these modern applications. Our research is developing novel mechanisms to address these challenges. We initially focus on the composition and configuration of the infrastructure hosting the individual services. We illustrate the use of domain-specific modeling languages and model weavers to model infrastructure composition using middleware building blocks, and to enhance these models with the desired performability attributes. We also demonstrate the use of generative tools that synthesize metadata from these models for performability validation using analytical, simulation and empirical benchmarking tools.