Clustering the objective interestingness measures based on tendency of variation in statistical implications
Journal Title: EAI Endorsed Transactions on Context-aware Systems and Applications - Year 2016, Vol 3, Issue 9
Abstract
In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application. Researchers in this field mainly focus on three main directions: clustering based on the properties of the measures, clustering based on the behavior of measures and clustering tendency of variation in statistical implications. In this paper we propose a new approach to cluster the objective interestingness measures based on tendency of variation in statistical implications. In this proposal, we built the statistical implication data of 31 objective interestingness measures based on the examination of the partial derivatives on four parameters. From this data, two distance matrices of interestingness measures are established based on Euclidean and Manhattan distance. The similarity trees are built based on distance matrix that gave results of 31 measures clustering with two different clustering thresholds.
Authors and Affiliations
Nghia Quoc Phan, Vinh Cong Phan, Hung Huu Huynh, Hiep Xuan Huynh
Keywords
Related Articles
- EP ID EP45782
- DOI http://dx.doi.org/10.4108/eai.2-5-2016.151212
- Views 270
- Downloads 0
Clustering the objective interestingness measures based on tendency of variation in statistical implications
In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application. Researchers in this field mainly focus o...
Context-aware Readings with Inbooki
E-books are more and more spreading among readers, who take advantage from the capability of reading them on different devices and of storing a whole library of books in a light and portable device. What is still missing...
Hybrid Algorithms of Whale optimization algorithm and k-nearest neighbor to Predict the liver disease
Liver Disease is one of the most common diseases which can be prevented by early diagnosis and up-todate treatment. Advances in machine learning and intelligence techniques have led to the effective diagnosis and predicti...
Mobility Patterns Mining Algorithms with Fast Speed
In recent years, mobile networks and its applications are developing rapidly. Therefore, the issue to ensure quality of service (QoS) is a key issue for the service providers. The movement prediction of Mobile Users (MUs...
Synchronous networks for bio-environmental surveillance based on cellular automata
The paper proposes a new approach to model a bio-environmental surveillance network as synchronous network systems, systems consist of components running simultaneously. In the network, bio-environmental factors compose...