Finding “interesting” trends in social networks using frequent pattern mining and self organizing maps

Nohuddin, Puteri N. E.; Coenen, Frans; Christley, Robert M.; Setzkorn, Christian; Patel, Yogesh C.; Williams, Shane

doi:10.1016/j.knosys.2011.07.003

Cited by 30 publications

(14 citation statements)

References 21 publications

(21 reference statements)

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“…Another study Arora and Badal [12], they applied Association Rule Mining in investigating academic performance of college students. This study will use the relationship regulation analysis technique [9,10,11] to look at the pattern of trends that exist in academic achievement among children of the B40 community.…”

Section: Background and Related Workmentioning

confidence: 99%

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Nohuddin¹,

Zainol²,

Hijazi³

2021

AETiC

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B40 community school children experience many underprivileged lifestyles which impacted their academic performance. Through data trends and patterns, the education top management can observe the progress of academic performance and the lifestyle relationships of students in a school or nearby school. It can also help to identify the causes of progress or deterioration of the performance of B40 community students. Therefore, a data analytics framework is essential to help decision-makers to see and analyze the changing trends and patterns of academic progress in data and related lifestyles of B40 community students more effectively and accurately. The objective of this study is to design and develop association rules analysis to deduce the relevance of academic achievement and lifestyle among schoolchildren from the B40 family. The analysis framework is established in several stages that involve data collection and processing and transformation, then the design, application and evaluate of the association rules algorithms. The framework is expected to benefit students, teachers and Education Ministry. This study foresees whether educational programs and healthy lifestyle awareness can be designed specifically for the B40 children so as to improve their academic achievement as desired by the government.

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Section: Background and Related Workmentioning

confidence: 99%

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Nohuddin¹,

Zainol²,

Hijazi³

2021

AETiC

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“…Experiments using a variety of network datasets (reported in Nohuddin et al . ()) have indicated that a large number of trends are often identified. Of course, the number of patterns to be considered can be reduced by using a higher support threshold, but the established argument against this expedient is that potential interesting patterns may be missed.…”

Section: Trend Identificationmentioning

confidence: 99%

Trend mining in social networks: from trend identification to visualization

et al. 2013

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A four‐stage social network trend mining framework, the Identification, Grouping, Clustering and Visualization framework, is described. The framework extracts trends from social network data and then applies a sequence of techniques (‘tools’) to this data to facilitate interpretation of the identified trends. Of particular note is the visualization of trend migrations (changes) that feature within time‐stamped network data. The framework is illustrated using a sequence of four social networks extracted from the Cattle Tracing System in operation in Great Britain, although it could equally well be applied to other forms of temporal data. The presented analysis of the Identification, Grouping, Clustering and Visualization framework indicates advantages, with respect to network trend mining, that can be gained, especially when the framework is applied to real data.

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“…It aims to extract the interesting relationship, frequent patterns and coloration existing in the set of items in the data repository [8]. ARM has been applied in many real world problems such as finding patterns in documents [9][10], predicting floods [11], trend analysis of social networks [12], monitoring elderly people [13], etc. Various research has been done to see the suitability of data mining in predicting the climate.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Climate Variability in Malaysia Using Association Rule Mining

Rashid¹,

Nohuddin²,

Zainol³

2018

IJET

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Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.

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Finding “interesting” trends in social networks using frequent pattern mining and self organizing maps

Cited by 30 publications

References 21 publications

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Trend mining in social networks: from trend identification to visualization

Analyzing Climate Variability in Malaysia Using Association Rule Mining

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