Mining Architecture Research Articles

Data mining based on computational intelligence

This paper combines computational intelligence tools: neural network, fuzzy logic, and genetic algorithm to develop a data mining architecture (NFGDM), which discovers patterns and represents them in understandable forms. In the NFGDM, input data are preprocessed by fuzzification, the preprocessed data of input variables are then used to train a radial basis probabilistic neural network to classify the dataset according to the classes considered. A rule extraction tech nique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if-then rules. In the final stage, genetic algorithm is used as a rule-pruning module to climinate those weak rules that are still in the rule bases. Comparison with some known neural network classifier, the architecture has fast learning speed, and it is characterized by the incorporation of the possibility information into the consequents of classification rules in human understandable forms. The experiments show that the NFGDM is more efficient and more robust than traditional decision tree method.

A Model-View-Controller ArchitectureFor Knowledge Discovery

In this paper we present a flexible mining architecture able to define and validate a process, generally applicable in different e-business sectors, for providing new added value e-Knowledge services. The architecture is designed on the ModelView-Controller pattern in order to get a clear separation of the component functionalities and covers the whole process of Knowledge Discovery in Databases (KDD) and in Text (KDT) for the extraction of patterns starting from structured and unstructured data. When a service request comes to the system, this is received by a Controller that will call one or more Miners to provide the results. The Miners represent the Model of the system and, by using a Kernel, dynamically activate either the KDD or the KDT process, depending on the typology of the service. As View, the system makes use of the CWM standard for the representation of metadata about models and results of the mining processes. The Kernel includes two different Focuses, for the selection of structured and unstructured data. The results of the Focuses are passed to a unique Pattern Extraction step, where Web and Data Mining algorithms are collected for the analysis. Finally, an Evaluation step interprets the utility of the extracted patterns. The proposed solutions can be suited in a distributed mining environment, where a set of services are managed and made available as a means of meeting the diverse needs of the e-business world.

Data-Mining-Anwendungsarchitektur

The ability to combine domain specific knowledge and special knowledge about using mathematical-statistical methods for analyzing big data bases at present time is not wide-spread in science and business. For the near future, an increase in data mining applications can be expected. So, one needs instruments to support non-specialists in using specific knowledege about data mining. In this paper a data mining architecture is introduced. Its main advantage is to offer a systematical scheme for data mining methods. These methods are structured with reference to applications. The data mining application architecture is a decision and structuring support for data mining problems to users, scientists and students.

Architecture for Web-Based Image Mining and Management Systems

The explosive growth in the volume of images available on the Web and in enterprise databases continues unabated. However, images on the Web are of many variations in type and format. Selecting relevant images based on image content is a difficult problem. Managing a large amount of images using their semantic meaning with content-based retrieval remains a challenge for both database and image processing researchers. This paper contributes by proposing an architecture for Web-based image mining and management systems with content-based image retrieval (CBIR), ImgWeb. ImgWeb is a Web-based system because it not only enables image mining and management for images in the database but also images from the Internet (Web images). Image agents play a key role in retrieving and mining Web images that are relevant to textual queries. ImgWeb is an object-oriented image database system, and it represents images as content-objects for processing. Thus, it supports CBIR and a wide range of graphical and textual querying methods. The proposed architecture permits the development of new image mining and management systems by utilizing existing components. Examples of image database applications include graphic design, recognition and entertainment.

Textual Data Mining to Support Science and Technology Management

This paper surveys applications of data mining techniques to large text collections, and illustrates how those techniques can be used to support the management of science and technology research. Specific issues that arise repeatedly in the conduct of research management are described, and a textual data mining architecture that extends a classic paradigm for knowledge discovery in databases is introduced. That architecture integrates information retrieval from text collections, information extraction to obtain data from individual texts, data warehousing for the extracted data, data mining to discover useful patterns in the data, and visualization of the resulting patterns. At the core of this architecture is a broad view of data mining—the process of discovering patterns in large collections of data—and that step is described in some detail. The final section of the paper illustrates how these ideas can be applied in practice, drawing upon examples from the recently completed first phase of the textual data mining program at the Office of Naval Research. The paper concludes by identifying some research directions that offer significant potential for improving the utility of textual data mining for research management applications.