Characteristics Of Data Mining Research Articles

Research and Application of Data Fusion and Data Mining in Early Warning Detection Intelligence Processing System

As a key technology of modern intelligence systems, Intelligence processing is a famous research area, and plays a basic role in the economy of the country especially in the early warning detection intelligence processing system. The basic theory of data fusion and date mining is comprehensively expounded, and real-time requirements of multi-target track data fusion, as well as offline demodulation timely information on characteristics of data mining, is deeply studied. Firstly, this paper discusses roundly the theory of association rules, which is an important research method of early warning intelligence data mining, proposes a new weighted probabilistic data association algorithm and improves joint probabilistic data association algorithm. Secondly, the knowledge of early warning intelligence data warehouse is detailed, and Law of cosines-text clustering algorithm based on distance is highlighted and extended. Finally, through the integration of real-time data integration and off-line data mining, the practical problems in information processing are effectively solved.

Análise de dados em artigos recuperados da Web of Science (WoS)

Dado o contexto da Mineração de Dados e da Mineração de Textos, objetiva-se analisar dados recuperados da Web of Science (WoS). Pretende-se identificar padrões nos estudos sobre Mineração de Textos voltados a escolha de ferramentas a serem utilizadas na aplicação de método de mineração de dados. Recuperaram-se referências de artigos no formato BibTeX na plataforma WoS. Desenvolveu-se uma aplicação para inserção de dados do formato BibTeX para um banco de dados MySQL. Com base nas características encontradas, elegeram-se a ferramenta R e algoritmo Apriori para utilização em parte dos dados. Extraíram-se dados de ferramentas, métodos, palavras-chave, termos, periódicos, países e autores presentes nos registros. A aplicação do Apriori resultou em treze regras de associação. A exploração dos dados de artigos provenientes da WoS revelou características dos estudos da área de Mineração de Textos. Trabalhos futuros podem adaptar a aplicação usada neste estudo e aplicar outros métodos de mineração no conjunto de dados.

A survey of open source data science tools

Purpose– Data science is the study of the generalizable extraction of knowledge from data. It includes a variety of components and develops on methods and concepts from many domains, containing mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization and data warehousing aiming to extract value from data. The purpose of this paper is to provide an overview of open source (OS) data science tools, proposing a classification scheme that can be used to study OS data science software.Design/methodology/approach– The proposed classification scheme is based on general characteristics, project activity, operational characteristics and data mining characteristics. The authors then use the proposed scheme to examine 70 identified Open Source Software. From this the authors provide insight about the current status of OS data science tools and reveal the state-of-the-art tools.Findings– The features of 70 OS tools are recorded based on the criteria of the four group characteristics, general characteristics, project activity, operational characteristics and data mining characteristics. Interesting results came from the analysis of these features and are recorded here.Originality/value– The contribution of this survey is development of a new classification scheme for examination and study of OS data science tools. In parallel, this study provides an overview of existing OS data science tools.

Detecting and identifying malicious executable binaries with Data Mining methods

The paper touches on the problem of improving vital characteristics of Data Mining - based systems responsible for detecting and identifying malicious executable binaries (malware). The common structure of learning and operating procedures for such systems is defined. The main non-functional requirements to the systems are specified on this structure's basis. The research's task is formulated as a look for a new, efficient representatin models for executable binaries. The models are to give compact, informative description vectors for such file objects. The essence of suggested approaches is expounded: the first one is focused on malware detection and based on positionally-dependent static data; the second uses dynamic low-level execution data for malware identification. The architecture of the developed system is represented as well as validation results for the developed representation models.

The Method of Attribute Reduction Based on Decision Tree

This paper is for refining database in the data mining process. Based on the analysis of the features and disadvantages of this decision tree algorithm and the substantive characteristics of data mining, we propose the concept of the core samples set and prove its invariance. On this basis, we build an attribute reduction method based on decision tree algorithm and then give a specific implementation steps, further, combined with a specific instance analyze the characteristics and efficiency of the method. Results show that the attribute reduction method based on the decision tree has good maneuverability and explicableness. This method can simply realize the attribute reduction of information system and its basic ideas completely adapt to the attribute reduction problems of the uncertain environment.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A survey of data mining and knowledge discovery software tools

Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. We propose a feature classification scheme that can be used to study knowledge and data mining software. This scheme is based on the software's general characteristics, database connectivity, and data mining characteristics. We then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. Finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either not addressed or insufficiently solved yet.