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      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <Volume-Issue>Volume 4, Issue 1 </Volume-Issue>
      <Season>March 2015</Season>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <Abstract>Advance in computing and communication over wired and wireless network have resulted in many distributive computing environments. Many of these environments have different distributed sources of voluminous data and multiple compute nodes. The Various science applications are typically at the forefront of large scale computing problems. &#13;
Fundamental scientific problems currently being explored generate gradually more complex data, require more realistic simulations of the processes under study and demand greater and more difficult visualizations of the results. These problems often require numerous complex calculations and collaboration among people with multiple disciplines and geographic locations. Examples of scientific grand challenge problems include multi-scale environmental modeling and ecosystem simulations, biomedical imaging and biomechanics, nuclear power and weapons simulations, fluid dynamics and fundamental computational science.&#13;
Grid computing has been proposed as a novel computational model, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high-performance orientation. Today grids can be used as effective infrastructures for distributed high-performance computing and data processing. The grid can play signi?cant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. &#13;
Data mining techniques can be efficiently deployed in a grid environment and operational grids can be mined for patterns that may help to optimize the effectiveness and efficiency of the grid computing infrastructure. This paper discusses how Grid computing can be used to support distributed data mining. Grid-based data mining uses Grids as decentralized high-performance platforms where to execute data mining tasks and knowledge discovery algorithms and applications.</Abstract>
      <Keywords>Grid Computing, Distributed Data Mining, Data mining algorithms, Knowledge Grid</Keywords>
        <Abstract>https://ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=3197&amp;title=DISTRIBUTED AND PARALLEL DATA MINING WITH GRID COMPUTING ENVIRONMENT</Abstract>