Intelligent Data Analysis

Deviating behavior within an organization can lead to unexpected results. The effects of deviations are often negative, but sometimes also positive. Therefore, it is useful to detect deviations from event logs which record all the behavior of the ...

Benchmarking is among the most widely adopted practices in business today. However, to the best of our knowledge, conducting multidimensional benchmarking in data warehouses has not been explored from a technical efficiency perspective. In this ...

A common task in data analysis is to find the appropriate data sample whose properties allow us to infer the parameters and behavior of the data population. In data mining this task makes sense since usually the population is significantly huge, ...

Conventional clustering algorithms optimize a single criterion, which may not conform to diverse needs of multidimensional data science. This paper proposes a new clustering algorithm that solves multiple clustering issues, called clustering by ...

Social media provides unprecedented opportunities for people to disseminate information and share their opinions and views online. Extracting events from social media platforms such as Twitter could help in understanding what is being discussed. ...

Cluster analysis or clustering is one of the most important and widely used techniques for data exploration and knowledge discovery that concerned with partitioning a set of objects in such a way that objects in the same groups, called clusters, ...

Multi-label learning has attracted significant attention from machine learning and data mining over the last decade. Although many multi-label classification algorithms have been devised, few research studies focus on multi-assignment clustering (...

Instance-based learning (IBL) methods predict the class label of a new instance based directly on the distance between the new unlabeled instance and each labeled instance in the training set, without constructing a classification model in the ...

Recruiting prospective students efficiently and effectively is a very important challenge for universities, mainly because of the increasing competition and the relevance of enrollment-generated revenues. This work provides an intelligent system ...

A Deep Belief Network (DBN) is a generative probabilistic graphical model that contains many layers of hidden variables and has excelled among deep learning approaches. DBN can extract suitable features, but improving these networks for obtaining ...

Two approaches to data mining with association rules are compared – the apriori algorithm and the ASSOC procedure. The first one was developed for market basket analysis at the beginning of 1990s. An association rule is understood as an ...

The problem of selecting learning algorithms has been studied by the meta-learning community for more than two decades. One of the most important task for the success of a meta-learning system is gathering data about the learning process. This ...