scholarly journals Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection

2013 ◽  
Vol 6 (2) ◽  
Author(s):  
Mirjana Maksimović ◽  
Vladimir Vujović
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Fire Detection ◽  
Wireless Sensor ◽  
Data Mining Algorithm ◽  
Critical Events ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Soft Computing Techniques

Wireless sensor networks (WSN) are a rapidly growing area for research and commercial development with very wide range of applications. Using WSNs many critical events like fire can be detected earlier to prevent loosing human lives and heavy structural damages. Integration of soft computing techniques on sensor nodes, like fuzzy logic, neural networks and data mining, can significantly lead to improvements of critical events detection possibility. Using data mining techniques in process of patterns discovery in large data sets it’s not often so easy. A several algorithms must be applied to application before a suitable algorithm for selected data types can be found. Therefore, the selection of a correct data mining algorithm depends on not only the goal of an application, but also on the compatibility of the data set. This paper focuses on comparative analysis of various data mining techniques and algorithms and in that purpose three different experiments on WSN fire detection data are proposed and performed. The primary goal was to see which of them has the best classification accuracy of fuzzy logic generated data and is the most appropriate for a particular application of fire detection.

Research Challenge of Locally Computed Ubiquitous Data Mining

Data Mining ◽  
2013 ◽  
pp. 1960-1978
Author(s):  
Aysegul Cayci ◽  
João Bártolo Gomes ◽  
Andrea Zanda ◽  
Ernestina Menasalvas ◽  
Santiago Eibe
Keyword(s):  
Data Mining ◽  
Situational Factors ◽  
Wireless Sensor ◽  
Data Mining Algorithm ◽  
Future Directions ◽  
Wearable Technologies ◽  
Mining Algorithm ◽  
New Challenges ◽  
Research Challenge

Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with intelligence. Autonomy and adaptability requirements are the two most important challenges for data mining in this new environment. In this chapter, in order to encourage the researchers on this area, we analyzed the challenges of designing ubiquitous data mining services by examining the issues and problems while paying special attention to context and resource awareness. We focused on the autonomous execution of a data mining algorithm and analyzed the situational factors that influence the quality of the result. Already existing solutions in this area and future directions of research are also covered in this chapter.

Research Challenge of Locally Computed Ubiquitous Data Mining

2011 ◽  
pp. 576-594
Author(s):  
Aysegul Cayci ◽  
João Bártolo Gomes ◽  
Andrea Zanda ◽  
Ernestina Menasalvas ◽  
Santiago Eibe
Keyword(s):  
Data Mining ◽  
Situational Factors ◽  
Wireless Sensor ◽  
Data Mining Algorithm ◽  
Future Directions ◽  
Wearable Technologies ◽  
Mining Algorithm ◽  
New Challenges ◽  
Research Challenge

Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with intelligence. Autonomy and adaptability requirements are the two most important challenges for data mining in this new environment. In this chapter, in order to encourage the researchers on this area, we analyzed the challenges of designing ubiquitous data mining services by examining the issues and problems while paying special attention to context and resource awareness. We focused on the autonomous execution of a data mining algorithm and analyzed the situational factors that influence the quality of the result. Already existing solutions in this area and future directions of research are also covered in this chapter.

Data Mining

2011 ◽  
pp. 921-926
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
New Technologies ◽  
Research Area ◽  
Future Research ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Degree Of Confidence ◽  
Common Application ◽  
Existing Data

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Ragged, 1999). There is a wide range of data mining techniques, which has been successfully used in many applications. This article is an attempt to provide an overview of existing data mining applications. The article begins by explaining the key tasks that data mining can achieve. It then moves to discuss applications domains that data mining can support. The article identifies three common application domains, including bioinformatics, electronic commerce, and search engines. For each domain, how data mining can enhance the functions will be described. Subsequently, the limitations of current research will be addressed, followed by a discussion of directions for future research.

scholarly journals A comparative analysis of classifiers in cancer prediction using multiple data mining techniques

2017 ◽  
Vol 1 (2) ◽  
pp. 166 ◽  
Author(s):  
Seyed Mohammad Jafar Jalali ◽  
Sérgio Moro ◽  
Mohammad Reza Mahmoudi ◽  
Keramat Allah Ghaffary ◽  
Mohsen Maleki ◽  
...  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Data Mining Techniques ◽  
Cancer Prediction ◽  
Multiple Data
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