Vincent Mwintieru Nofong- Doctor of Philosophy
- Senior Lecturer at University of Mines and Technology
Vincent Mwintieru Nofong
- Doctor of Philosophy
- Senior Lecturer at University of Mines and Technology
About
19
Publications
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Introduction
Current institution
Additional affiliations
May 2016 - present
February 2012 - April 2016
Education
February 2012 - February 2016
Publications
Publications (19)
Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with hetero...
This paper studies the entity resolution (ER) problem in property graphs. ER is the task of identifying and linking different records that refer to the same real-world entity. It is commonly used in data integration, data cleansing, and other applications where it is important to have accurate and consistent data. In general, two predominant approa...
This paper studies the discovery of approximate rules in property graphs. First, we propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) that almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. Second, we present a new characterisation of GED satisfaction, and devise a depth...
This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. We present a new characterisation of GED satisfaction, and devise a depth-first search str...
Periodic frequent pattern discovery is a non-trivial task for analysing databases to reveal the recurring shapes of patterns’ occurrences. Though significant strides have been made in their discovery for understanding large databases in decision-making, existing techniques still face a challenge of reporting a large number of periodic frequent patt...
Periodic frequent patterns are frequent patterns which occur at periodic intervals in databases. They are useful in decision making where event occurrence intervals are vital. Traditional algorithms for discovering periodic frequent patterns, however, often report a large number of such patterns, most of which are often redundant as their periodic...
Discovering periodic frequent patterns has been useful in various decision making. Traditional algorithms, however, often report a large number of such patterns, most of which are often redundant since their periodic occurrences can be inferred from other periodic frequent patterns. Employing such redundant periodic frequent patterns in decision ma...
Periodic frequent pattern (PFP) mining, the process of discovering frequent patterns that occur at regular periods in databases, is an important data mining task for various decision-making. Although several algorithms have been proposed for discovering PFPs, most of these algorithms often employ a two-stage approach to mining these periodic freque...
Periodic frequent pattern mining, the process of finding frequent patterns which occur periodically in databases, is an important data mining task for various decision making. Though several algorithms have been proposed for their discovery, most employ a two stage process to evaluate the periodicity of patterns. That is, by firstly deriving the se...
Periodic frequent pattern mining is an important data mining task for various decision making. However, it often presents a large number of periodic frequent patterns, most of which are not useful as their periodicities are due to random occurrence of uncorrelated items. Such periodic frequent patterns would most often be detrimental in decision ma...
Emerging patterns are patterns whose frequencies increase from one dataset to another. They can reveal useful trends and contrasts in datasets to support decision making such as trend prediction and classification. However, current works mostly focus on discovering emerging patterns for classification and seldom discuss their use in time-stamped da...
Periodic frequent pattern mining is an important data mining task for various decision making. However, it often presents a large number of periodic frequent patterns, most of which are not useful as their periodicities are due to random occurrence of uncorrelated items. Such periodic frequent patterns would most often be detrimental in decision ma...
Emerging pattern mining is an important data mining task for various decision making. However, it often presents a large number of emerging patterns most of which are not useful as their emergence are due to random occurrence of items. Such emerging patterns would most often be detrimental in decision making where inherent relationships between the...
Emerging pattern mining is an important data mining task for various decision making. However, it often presents a large number of emerging patterns, most of which are not useful as their emergence are derivable from other emerging patterns. Such derivable emerging patterns most often are trivial in decision making. To enable mine the set of non-de...
This article presents a study on the techniques for detecting Emerging Sequential Patterns (ESPs) and the effectiveness of predictions made by ESPs in time-stamped datasets. ESPs are sequential patterns whose frequencies increase from one time-stamp dataset to another. ESPs capture emerging trends with time in sequential datasets and they are propo...
