Incorporating Data Mining Tools into a New Hybrid-IDS to Detect Known and Unknown Attacks

2006 ◽  
pp. 826-834 ◽  
Author(s):  
Lokesh D. Pathak ◽  
Ben Soh
Keyword(s):  
Data Mining ◽  
Mining Tools

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

DATA MINING FOR THE MANAGEMENT OF SOFTWARE DEVELOPMENT PROCESS

2004 ◽  
Vol 14 (06) ◽  
pp. 665-695 ◽  
Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS
Keyword(s):  
Data Mining ◽  
Software Development ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Development Process ◽  
A Priori ◽  
Post Mortem ◽  
Software Development Process ◽  
A Priori Analysis ◽  
Mining Tools

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.

scholarly journals Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

2015 ◽  
Vol 64 (1) ◽  
pp. 49-64 ◽  
Author(s):  
László Pásztor ◽  
Annamária Laborczi ◽  
Katalin Takács ◽  
Gábor Szatmári ◽  
Endre Dobos ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Maps ◽  
Mining Tools ◽  
Digital Soil Maps

scholarly journals Predicting Academic Course Preference using Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 2527-2530
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
New Technologies ◽  
Parallel Applications ◽  
Distributed File System ◽  
Industrial Training ◽  
Effective Learning ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Mining Tools

These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.

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