scholarly journals Data mining using two-dimensional optimized association rules

1996 ◽  
Author(s):  
Takeshi Fukuda ◽  
Yasukiko Morimoto ◽  
Shinichi Morishita ◽  
Takeshi Tokuyama
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Two Dimensional

scholarly journals Data mining using two-dimensional optimized association rules

1996 ◽  
Vol 25 (2) ◽  
pp. 13-23 ◽  
Author(s):  
Takeshi Fukuda ◽  
Yasukiko Morimoto ◽  
Shinichi Morishita ◽  
Takeshi Tokuyama
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Two Dimensional

Data Mining with optimized two-dimensional association rules

2001 ◽  
Vol 26 (2) ◽  
pp. 179-213 ◽  
Author(s):  
Takeshi Fukuda ◽  
Yasuhiko Morimoto ◽  
Shinichi Morishita ◽  
Takeshi Tokuyama
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Two Dimensional

How Useful Can Be Data Mining For A Continuos Speech Therapist’s Education?

2014 ◽  
Vol 1 (1) ◽  
pp. 339-342
Author(s):  
Mirela Danubianu ◽  
Dragos Mircea Danubianu
Keyword(s):  
Data Mining ◽  
Information And Communication Technology ◽  
Association Rules ◽  
Communication Technology ◽  
Speech Therapy ◽  
Proper Treatment ◽  
Speech Impairments ◽  
Information And Communication ◽  
Specific Education

AbstractSpeech therapy can be viewed as a business in logopaedic area that aims to offer services for correcting language. A proper treatment of speech impairments ensures improved efficiency of therapy, so, in order to do that, a therapist must continuously learn how to adjust its therapy methods to patient's characteristics. Using Information and Communication Technology in this area allowed collecting a lot of data regarding various aspects of treatment. These data can be used for a data mining process in order to find useful and usable patterns and models which help therapists to improve its specific education. Clustering, classification or association rules can provide unexpected information which help to complete therapist's knowledge and to adapt the therapy to patient's needs.

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

scholarly journals Understanding Structure of Poverty Dimensions in East Java: Bicluster Approach

2017 ◽  
Vol 6 (2) ◽  
pp. 289-300 ◽  
Author(s):  
Budi Yuniarto ◽  
Robert Kurniawan
Keyword(s):  
Data Mining ◽  
Poverty Reduction ◽  
Multidimensional Poverty ◽  
Two Dimensional ◽  
View Point ◽  
Dimensional Matrix ◽  
Clustering Patterns

Poverty is still become a main problem for Indonesia, where recently, the view point of poverty is not just from income or consumption, but it’s defined multidimensionally. The understanding of the structure of multidimensional poverty is essential to government to develop policies for poverty reduction. This paper aims to describe the structure of poverty in East Java by using variables forming the dimensions of poverty and to investigate any clustering patterns in the region of East Java with considering the poverty variables using biclustering method. Biclustering is an unsupervised technique in data mining where we are grouping scalars from the two-dimensional matrix. Using bicluster analysis, we found two bicluster where each bicluster has different characteristics.DOI: 10.15408/sjie.v6i2.4769

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