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Search Results: 1 - 10 of 16362 matches for " Data mining "
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Data Mining Technology across Academic Disciplines  [PDF]
Lesley Farmer, Alan Safer, Eric Chuk
Intelligent Information Management (IIM) , 2011, DOI: 10.4236/iim.2011.32005
Abstract: University courses in data mining across the United States are taught primarily in departments of business, computer science/engineering, statistics, and library/information science. Faculty in each of these departments teach data mining with a unique emphasis, although there is considerable overlap relative to course offerings, terminology, technology, resources, and faculty publications. Content analysis research aims to describe in detail the range of data mining technology differences and overlap across academic disciplines.
Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects  [PDF]
Tim MENZIES, Osamu MIZUNO, Yasunari TAKAGI, Tohru KIKUNO
Journal of Software Engineering and Applications (JSEA) , 2009, DOI: 10.4236/jsea.2009.24030
Abstract: Often, the explanatory power of a learned model must be traded off against model performance. In the case of predict-ing runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in predicting runaway projects. Measured in terms of precision, this new model is as good as can be expected for our data. Other methods might out-perform our result (e.g. by generating a smaller, more explainable model) but no other method could out-perform the precision of our learned model.
Data Mining in Biomedicine: Current Applications and Further Directions for Research  [PDF]
S. L. TING, C. C. SHUM, S. K. KWOK, A. H. C. TSANG, W. B. LEE
Journal of Software Engineering and Applications (JSEA) , 2009, DOI: 10.4236/jsea.2009.23022
Abstract: Data mining is the process of finding the patterns, associations or relationships among data using different analytical techniques involving the creation of a model and the concluded result will become useful information or knowledge. The advancement of the new medical deceives and the database management systems create a huge number of data-bases in the biomedicine world. Establishing a methodology for knowledge discovery and management of the large amounts of heterogeneous data has become a major priority of research. This paper introduces some basic data mining techniques, unsupervised learning and supervising learning, and reviews the application of data mining in biomedicine. Applications of the multimedia mining, including text, image, video and web mining are discussed. The key issues faced by the computing professional, medical doctors and clinicians are highlighted. We also state some foreseeable future developments in the field. Although extracting useful information from raw biomedical data is a challenging task, data mining is still a good area of scientific study and remains a promising and rich field for research.
Data Categorization and Noise Analysis in Mobile Communication Using Machine Learning Algorithms  [PDF]
Raghavendra Phani Kumar, Malleswara Rao, Dsvgk Kaladhar
Wireless Sensor Network (WSN) , 2012, DOI: 10.4236/wsn.2012.44015
Abstract: Machine learning and pattern recognition contains well-defined algorithms with the help of complex data, provides the accuracy of the traffic levels, heavy traffic hours within a cluster. In this paper the base stations and also the noise levels in the busy hour can be predicted. J48 pruned tree contains 23 nodes with busy traffic hour provided in east Godavari. Signal to noise ratio has been predicted at 55, based on CART results. About 53% instances provided inside the cluster and 47% provided outside the cluster. DBScan clustering provided maximum noise from srikakulam. MOR (Number of originating calls successful) predicted as best associated attribute based on Apriori and Genetic search 12:1 ratio.
A State-of-the-Art Survey on Semantic Web Mining  [PDF]
Qudamah K. Quboa, Mohamad Saraee
Intelligent Information Management (IIM) , 2013, DOI: 10.4236/iim.2013.51002
Abstract: The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Semantic Web Mining. The huge increase in the amount of Semantic Web data became a perfect target for many researchers to apply Data Mining techniques on it. This paper gives a detailed state-of-the-art survey of on-going research in this new area. It shows the positive effects of Semantic Web Mining, the obstacles faced by researchers and propose number of approaches to deal with the very complex and heterogeneous information and knowledge which are produced by the technologies of Semantic Web.
Data Mining of Historic Hydrogeological and Socioeconomic Data Bases of the Toluca Valley, Mexico  [PDF]
Oliver López-Corona, Oscar Escolero Fuentes, Eric Morales-Casique, Pablo Padilla Longoria, Tomás González Moran
Journal of Water Resource and Protection (JWARP) , 2016, DOI: 10.4236/jwarp.2016.84044
Abstract: In this paper we used several data mining techniques to analyze the coevolution of hydrogeological and socioeconomical data for the Toluca Valley in Mexico. We found non trivial relations between two historic data bases that make clear that groundwater and economy may be much more linked than it was thought before. In particular, we found that hydrogeological data trends change during economical crisis and election years in Mexico. This shows that different macroeconomical policies implemented by several administrations have a direct impact in the way groundwater is used. We also found that hydrogoelogical data evolve in the direction of population transformation from rural to urban, which could represent a whole paradigm shift in groundwater management with profound repercussions in policy making.
Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset  [PDF]
Animesh Tripathy, Ipsa De
Journal of Software Engineering and Applications (JSEA) , 2013, DOI: 10.4236/jsea.2013.65B006
Abstract: Data mining has been a popular research area for more than a decade. There are several problems associated with data mining. Among them clustering is one of the most interesting problems. However, this problem becomes more challenging when dataset is distributed between different parties and they do not want to share their data. So, in this paper we propose a privacy preserving two party hierarchical clustering algorithm vertically partitioned data set. Each site only learns the final cluster centers, but nothing about the individual’s data.
ANALYSIS METHODS OF WORKFLOW EXECUTION FOR DATA BASED ON ROUGH SET THEORY IN DATA MINING
Anoop Shrivastava
International Journal of Advanced Technology & Engineering Research , 2012,
Abstract: Exploring the trivial workflow data needs high performance data processing technology. In this research work we put forward analysis method of workflow execution data based on data mining. The main idea of it is to retrieve the workflow data to a data warehouse and adopt OLAP technology and data mining method to support customers to select different measures and view the corresponding data in different dimensions and different abstract levels, which is important for them to make decision. This research work presents the use of a relatively new method, the Rough Set (RS) theory for knowledge acquisition in time sequence condition monitoring. An additional attraction of the RS theory is that it allows automated generation of knowledge models, offering clear explanations to the inferences performed in diagnosis.
Mining Positive and Negative Association Rules Using CoherentApproach
RAKESH DUGGIRALA 1 , P.NARAYANA 2
International Journal of Computer Trends and Technology , 2013,
Abstract: From the coherent rules discovered, association rules can be derived objectively and directly without knowing the level of minimum support threshold required. We provide analysis of the rules compare to those discovered via the apriori. The framework is developed based on implication of propositional logic via Negative and positive association algorithm. The experiments show that our approach is able to identify meaningful association rules within an acceptable execution time. This framework develop a new algorithm based on coherent rules so that users can mine the items without domain knowledge and it can mine the items efficiently when compared to association rules.
Biology Inspired Image Segmentation using Methods of Artificial Intelligence  [PDF]
Radim Burget, Vaclav Uher, Jan Masek
Journal of Software Engineering and Applications (JSEA) , 2012, DOI: 10.4236/jsea.2012.512B033
Abstract: In recent years,many efforts have been devoted to image segmentation. Although for a man general image segmentation is considered an easy task, for computers it is still considered to be difficult, computationally intensive and still unresolved task. This work presents an innovative algorithm combining theory of artificial intelligence and knowledge of human eye anatomy. The resulting algorithm has not ambitions to be universal like human brain but can be trained and perform on selected domain. The effectiveness of the algorithm is demonstrated on the selected examples.
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