Association Rule Mining Using Time Series Data for Malaysia Climate Variability Prediction

2017 ◽  
pp. 120-130
Author(s):  
Rabiatul A. A. Rashid ◽  
Puteri N. E. Nohuddin ◽  
Zuraini Zainol
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Series Data ◽  
Series Data ◽  
Rule Mining

scholarly journals Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

2018 ◽  
Vol 7 (3.3) ◽  
pp. 218 ◽  
Author(s):  
D Senthil ◽  
G Suseendran
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Series Data ◽  
Sliding Window ◽  
Series Data ◽  
Support Vector ◽  
Rule Mining ◽  
Window Technique

Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.  

Association Rule Mining in Developmental Psychology

Data Mining ◽  
2013 ◽  
pp. 1737-1751
Author(s):  
D. A. Nembhard ◽  
K. K. Yip ◽  
C. A. Stifter
Keyword(s):  
Time Series ◽  
Association Rule ◽  
Association Rule Mining ◽  
Developmental Psychology ◽  
Temporal Characteristic ◽  
Data Sets ◽  
Rule Mining ◽  
Dynamic Interactions ◽  
Series Type ◽  
Type Data

Developmental psychology is the scientific study of progressive psychological changes that occur in human beings as they age. Some of the current methodologies used in this field to study developmental processes include Yule’s Q, state space grids, time series analysis, and lag analysis. The data collected in this field are often time-series-type data. Applying association rule mining in developmental psychology is a new concept that may have a number of potential benefits. In this paper, two sets of infant-mother interaction data sets are examined using association rule mining. Previous analyses of these data used conventional statistical techniques. However, they failed to capture the dynamic interactions between the infant-mother pair as well as other issues relating to the temporal characteristic of the data. Three approaches are proposed in this paper as candidate means of addressing some of the questions that remain from previous studies. The approaches used can be applied to association rule mining to extend its application to data sets in related fields.

scholarly journals Analyzing Climate Variability in Malaysia Using Association Rule Mining

2018 ◽  
Vol 7 (4.34) ◽  
pp. 394
Author(s):  
Rabiatul A. A. Rashid ◽  
Puteri N. E. Nohuddin ◽  
Zuraini Zainol
Keyword(s):  
Climate Change ◽  
Data Mining ◽  
Climate Variability ◽  
Association Rule ◽  
Association Rule Mining ◽  
Climate Data ◽  
Rule Mining ◽  
Clustering And Classification ◽  
Change Climate ◽  
Climate Uncertainty

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.  

Association Rule Mining in Developmental Psychology

2012 ◽  
Vol 1 (1) ◽  
pp. 23-37 ◽  
Author(s):  
D. A. Nembhard ◽  
K. K. Yip ◽  
C. A. Stifter
Keyword(s):  
Time Series ◽  
Association Rule ◽  
Association Rule Mining ◽  
Developmental Psychology ◽  
Temporal Characteristic ◽  
Data Sets ◽  
Human Beings ◽  
Rule Mining ◽  
Dynamic Interactions ◽  
Series Type

Developmental psychology is the scientific study of progressive psychological changes that occur in human beings as they age. Some of the current methodologies used in this field to study developmental processes include Yule’s Q, state space grids, time series analysis, and lag analysis. The data collected in this field are often time-series-type data. Applying association rule mining in developmental psychology is a new concept that may have a number of potential benefits. In this paper, two sets of infant-mother interaction data sets are examined using association rule mining. Previous analyses of these data used conventional statistical techniques. However, they failed to capture the dynamic interactions between the infant-mother pair as well as other issues relating to the temporal characteristic of the data. Three approaches are proposed in this paper as candidate means of addressing some of the questions that remain from previous studies. The approaches used can be applied to association rule mining to extend its application to data sets in related fields.

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