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.

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 Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

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.  

APPLYING ASSOCIATION MINING TO CHANGE PROPAGATION

2008 ◽  
Vol 18 (08) ◽  
pp. 1043-1061 ◽  
Author(s):  
LIGUO YU ◽  
STEPHEN R. SCHACH
Keyword(s):  
Association Rule ◽  
Software Maintenance ◽  
Association Rule Mining ◽  
Concept Drift ◽  
Training Data ◽  
Software Systems ◽  
Association Mining ◽  
Change Propagation ◽  
Data Sets ◽  
Rule Mining

A software system evolves as changes are made to accommodate new features and repair defects. Software components are frequently interdependent, so changes made to one component can result in changes having to be made to other components to ensure that the system remains consistent; this is called change propagation. Accurate detection of change propagation is essential for software maintenance, which can be aided by accurate prediction of change propagation. In this paper, we study change propagation in three leading open-source software products: Linux, FreeBSD, and Apache HTTP Server. We use association rules-based data-mining techniques to detect change-propagation rules from the product version history. These rules are evaluated with respect to different training data sets and different test data sets. We discuss the applicability of using association-rule mining for change propagation, and several related issues. We find that a challenging issue in association-rule mining, concept drift, exists in software systems. Concept drift complicates the task of change-propagation prediction and requires special approaches, different from currently-used techniques for predicting change propagation.

The Novel Model of Construct Materials Science and Information Based on Association Rule Mining

2013 ◽  
Vol 327 ◽  
pp. 197-200
Author(s):  
Guo Fang Kuang ◽  
Ying Cun Cao
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Materials Science ◽  
Frequent Itemsets ◽  
Data Sets ◽  
The Novel ◽  
Rule Mining ◽  
Novel Model

The material is used by humans to manufacture the machines, components, devices and other products of substances. Association rules originated in the field of data mining, people use it to find large amounts of data between itemsets of the association. Apriori is a breadth-first algorithm to obtain the support is greater than the minimum support of frequent itemsets by repeatedly scanning the database. This paper presents the construction of materials science and information model based on association rule mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals AN OPTIMIZED ARM SCHEME FOR DISTINCT NETWORK DATA SET

2015 ◽  
pp. 191-195
Author(s):  
K.GANESH KUMAR ◽  
H.VIGNESH RAMAMOORTHY ◽  
M.PREM KUMAR ◽  
S. SUDHA
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
Research Area ◽  
Sequential Algorithm ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Communication Costs

Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.

scholarly journals Association Rule Mining on Big Data Sets

2020 ◽  
Author(s):  
Oguz Celik ◽  
Muruvvet Hasanbasoglu ◽  
Mehmet S. Aktas ◽  
Oya Kalipsiz
Keyword(s):  
Big Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Sets ◽  
Rule Mining
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