Agents and data mining: mutual enhancement by integration

This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.

Robust-diagnostic regression : a prelude for inducing reliable knowledge from regression

Regression lies heart in statistics, it is the one of the most important branch of multivariate techniques available for extracting knowledge in almost every field of study and research. Nowadays, it has drawn a huge interest to perform the tasks with different fields like machine learning, pattern recognition and data mining. Investigating outlier (exceptional) is a century long problem to the data analyst and researchers. Blind application of data could have dangerous consequences and leading to discovery of meaningless patterns and carrying to the imperfect knowledge. As a result of digital revolution and the growth of the Internet and Intranet data continues to be accumulated at an exponential rate and thereby importance of detecting outliers and study their costs and benefits as a tool for reliable knowledge discovery claims perfect attention. Investigating outliers in regression has been paid great value for the last few decades within two frames of thoughts in the name of robust regression and regression diagnostics. Robust regression first wants to fit a regression to the majority of the data and then to discover outliers as those points that possess large residuals from the robust output whereas in regression diagnostics one first finds the outliers, delete/correct them and then fit the regular data by classical (usual) methods. At the beginning there seems to be much confusion but now the researchers reach to the consensus, robustness and diagnostics are two complementary approaches to the analysis of data and any one is not good enough. In this chapter, we discuss both of them under the unique spectrum of regression diagnostics. Chapter expresses the necessity and views of regression diagnostics as well as presents several contemporary methods through numerical examples in linear regression within each aforesaid category together with current challenges and possible future research directions. Our aim is to make the chapter self-explained maintaining its general accessibility.

Image processing and pattern recognition in textiles

Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into / with artificial neural networks, the image processing technique has provided a useful tool to solve complex problems in textile technology. Three different approaches are used in this work forfiber classification and pattern recognition: feature extraction with image process, pattern recognition and classification with artificial neural networks, and feature recognition and classification with artificial neural network. All of them yieldssatisfactory results by giving a high level of accuracy in classification.

Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

Using data mining for digital ink recognition: dividing text and shapes in sketched diagrams

The low accuracy rates of textshape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing ink. Feature based recognition is a common approach for textshape division. However, the choice of features and algorithms are critical to the success of the recognition. We propose the use of data mining techniques to build more accurate textshape dividers. A comparative study is used to systematically identify the algorithms best suited for the specific problem. We have generated dividers using data mining with diagrams from three domains and a comprehensive ink feature library. The extensive evaluation on diagrams from six different domains has shown that our resulting dividers, using LADTree and LogitBoost, are significantly more accurate than three existing dividers.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Agents and stream data mining: a new perspective

Many organizations struggle with the massive amount of data they collect. Today, data does more than serve as the ingredients for churning out statistical reports. They help support efficient operations in many organizations, and to some extent, data provide the competitive intelligence organizations need to survive in today's economy. Data mining can't always deliver timely and relevant results because data are constantly changing. However, stream-data processing might be more effective, judging by the Matrix project.

Software warehouse and software mining : the impact of data mining to software engineering

Automating Software Engineering is the dream of software Engineers for decades. To make this dream to come to true, data mining can play an important role. Our recent research has shown that to increase the productivity and to reduce the cost of software development, it is essential to have an effective and efficient mechanism to store, manage and utilize existing software resources, and thus to automate software analysis, testing, evaluation and to make use of existing software for new problems. This paper firstly provides a brief overview of traditional data mining followed by a presentation on data mining in broader sense. Secondly, it presents the idea and the technology of software warehouse as an innovative approach in managing software resources using the idea of data warehouse where software assets are systematically accumulated, deposited, retrieved, packaged, managed and utilized driven by data mining and OLAP technologies. Thirdly, we presented the concepts and technology and their applications of data mining and data matrix including software warehouse to software engineering. The perspectives of the role of software warehouse and software mining in modern software development are addressed. We expect that the results will lead to a streamlined high efficient software development process and enhance the productivity in response to modern challenges of the design and development of software applications.

Research on seismic signals for vehicle targets and recognition by data fusion

This paper researches seismic signals of typical vehicle targets in order to extract features and to recognize vehicle targets. As a data fusion method, the technique of artificial neural networks combined with genetic algorithm(ANNCGA) is applied for recognition of seismic signals that belong to different kinds of vehicle targets. The technique of ANNCGA and its architecture have been presented. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that seismic properties of target acquired are correct, ANNCGA data fusion method is effective to solve the problem of target recognition.