2,552 research outputs found

    Web Mining for Web Personalization

    Get PDF
    Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

    Data Mining in Hospital Information System

    Get PDF

    A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

    Get PDF
    Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining

    An Internet-enabled Knowledge Discovery Process

    Get PDF

    New Fundamental Technologies in Data Mining

    Get PDF
    The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

    Extractive text summarisation using graph triangle counting approach: proposed method

    Get PDF
    Currently, with a growing quantity of automated text data, the necessity for the con-struction of Summarisation systems turns out to be vital. Summarisation systems confine and condense the mainly vital ideas of the papers and assist the user to find and understand the foremost facts of the text quicker and easier from the dispensation of information. Compelling set of such systems are those that create summaries of ex-tracts. This type of summary, which is called Extractive Summarisation , is created by choosing large significant fragments of the text without making any amendment to the original. One methodology for generating this type of summary is consuming the graph theory. In graph theory there is one field called graph pruning / reduction, which means, to find the best representation of the main graph with a smaller number of nodes and edges. In this paper, a graph reduction technique called the triangle counting approach is presented to choose the most vital sentences of the text. The first phase is to represent a text as a graph, where nodes are the sentences and edges are the similarity between the sentences. The second phase is to construct the triangles, after that bit vector representation and the final phase is to retrieve the sentences based on the values of bit vector
    • …
    corecore