The rate at which organizations are acquiring data is exploding and managing such data so as to infer useful knowledge that can be put to use is increasingly becoming important. Data Mining (DM) is one such technology that is employed in inferring useful knowledge that can be put to use from a vast amount of data. This paper presents the data mining activity that was employed in weather data pr...

The history of mining disasters includes cases of subsidence and collapse into workings, and also a number of inrushes into underground mines, where deficiencies in surveying, or in maintenance and interpretation of plans, were prime factors. The most recent incident in Australia was at the Gretley Colliery in NSW, in 1996. The most recent such incident in the USA was at the Quecreek Mine in So...

Information network mining has various applications in viral marketing, link prediction, recommendation and many other aspects. Traditionally, information network mining assumes a discrete time step in activity logs, thus is not capable to do continuous time predictions. Secondly, most information network mining models and algorithms only consider pairwise influence. According to our observatio...

Today, multi-electrode arrays (MEAs) capture neuronal spike streams in real time, thus providing dynamic perspectives into brain function. Mining such spike streams from these MEAs is critical towards understanding the firing patterns of neurons and gaining insight into the underlying cellular activity. However, the acquisition rate of neuronal data places a tremendous computational burden on t...

In this study, we collected rhizosphere soils and root samples from a post-mining area and a natural forest area in Jecheon, Korea. We extracted spores of arbuscular mycorrhizal fungi (AMF) from rhizospheres, and then examined the sequences of 18S rDNA genes of the AMF from the collected roots of plants. We compared the AMF communities in the post-mining area and the natural forest area by sequ...

Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic, perspectives into brain function. Mining event streams from these chips is critical to understanding the firing patterns of neurons and to gaining insight into the underlying cellular activity. We present a GPGPU solution to mining spike trains. We focus on mining freque...

The goal of an intrusion detection system (IDS) is to identify authorized and unauthorized intruders by differentiating anomalous network activity from normal network traffic. Data mining methods have been used to build automatic intrusion detection systems. The central idea is to utilize auditing programs to extract a set of features that describe each network connection or host session, and a...

association rules are among important techniques in data mining which are used for extracting hidden patterns and knowledge in large volumes of data. association rules help individuals and organizations take strategic decisions and improve their business processes. extracted association rules from a database contain important and confidential information that if published, the privacy of indivi...

Predictive data mining in clinical medicine deals with learning models to predict patients’ health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks. Data mining methods are usually applied in clinical contexts to analyze retrospective data, thus giving healthcare professionals the opportunity to exploit large amounts of data routinely collected du...

due to the quick growth of the world wide web, retrieval of useful informationfrom the internet for a particular web user or a group of users becomes verydifficult. recommendation systems using web usage mining help providing anadaptive web environment for the web users. this paper presents a novel approachfor page recommendation using fuzzy association rule mining algorithm. thismethod extract...