EFFECTIVE AND EFFICIENT VIDEO HIGH-LEVEL SEMANTIC RETRIEVAL USING ASSOCIATIONS AND CORRELATIONS

2009 ◽  
Vol 03 (04) ◽  
pp. 421-444 ◽  
Author(s):  
LIN LIN ◽  
MEI-LING SHYU
Keyword(s):  
Association Rule ◽  
High Efficiency ◽  
Multiple Correspondence Analysis ◽  
Data Sets ◽  
Semantic Retrieval ◽  
Rule Mining ◽  
The Mean ◽  
Concept Classes ◽  
High Level ◽  
Efficient Video

Two important approaches in multimedia information retrieval are classification and the ranking of the retrieved results. The technique of performing classification using Association Rule Mining (ARM) has been utilized to detect the high-level features from the video, taking advantages of its high efficiency and accuracy. Motivated by the fact that the users are only interested in the top-ranked relevant results, ranking strategies have been adopted to sort the retrieved results. In this paper, an effective and efficient video high-level semantic retrieval framework that utilizes associations and correlations to retrieve and rank the high-level features is developed. The n-feature-value pair rules are generated using a combined measure based on (1) the existence of the (n - 1)-feature-value pairs, where n is larger than 1, (2) the correlation between different n-feature-value pairs and the concept classes through Multiple Correspondence Analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intra-similarity. The final association classification rules are selected by using the calculated similarity values. Then our proposed ranking process uses the scores that integrate the correlation and similarity values to rank the retrieved results. To show the robustness of the proposed framework, experiments with 15 high-level features (concepts) and benchmark data sets from TRECVID and comparisons with 6 other well-known classifiers are presented. Our proposed framework achieves promising performance and outperforms all the other classifiers. Moreover, the final ranked retrieved results are evaluated by the mean average precision measure, which is commonly used for performance evaluation in the TRECVID community.

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 Construction Search Engine Based on Formal Concept Analysis and Association Rule Mining

2012 ◽  
Vol 6-7 ◽  
pp. 625-630 ◽  
Author(s):  
Hong Sheng Xu
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Formal Concept Analysis ◽  
Association Rule Mining ◽  
High Efficiency ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Frequent Itemsets ◽  
Formal Concept ◽  
Rule Mining

In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.

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.

scholarly journals Parametric Rough Sets with Application to Granular Association Rule Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xu He ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Recommender Systems ◽  
Association Rule ◽  
Rough Sets ◽  
Relational Databases ◽  
Data Sets ◽  
Rule Mining ◽  
Real World Data ◽  
New Model ◽  
New Type ◽  
Two Universes

Granular association rules reveal patterns hidden in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold-start recommendation, where a customer or a product has just entered the system. An example of such rules might be “40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol.” Mining such rules is a challenging problem due to pattern explosion. In this paper, we build a new type of parametric rough sets on two universes and propose an efficient rule mining algorithm based on the new model. Specifically, the model is deliberately defined such that the parameter corresponds to one threshold of rules. The algorithm benefits from the lower approximation operator in the new model. Experiments on two real-world data sets show that the new algorithm is significantly faster than an existing algorithm, and the performance of recommender systems is stable.

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

scholarly journals Awareness and preparedness of healthcare workers against the first wave of the COVID-19 pandemic: A cross-sectional survey across 57 countries

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0258348
Author(s):  
Nguyen Tien Huy ◽  
R. Matthew Chico ◽  
Vuong Thanh Huan ◽  
Hosam Waleed Shaikhkhalil ◽  
Vuong Ngoc Thao Uyen ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Healthcare Workers ◽  
Management Training ◽  
Mean Difference ◽  
Rule Mining ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Training Courses ◽  
High Level

Background Since the COVID-19 pandemic began, there have been concerns related to the preparedness of healthcare workers (HCWs). This study aimed to describe the level of awareness and preparedness of hospital HCWs at the time of the first wave. Methods This multinational, multicenter, cross-sectional survey was conducted among hospital HCWs from February to May 2020. We used a hierarchical logistic regression multivariate analysis to adjust the influence of variables based on awareness and preparedness. We then used association rule mining to identify relationships between HCW confidence in handling suspected COVID-19 patients and prior COVID-19 case-management training. Results We surveyed 24,653 HCWs from 371 hospitals across 57 countries and received 17,302 responses from 70.2% HCWs overall. The median COVID-19 preparedness score was 11.0 (interquartile range [IQR] = 6.0–14.0) and the median awareness score was 29.6 (IQR = 26.6–32.6). HCWs at COVID-19 designated facilities with previous outbreak experience, or HCWs who were trained for dealing with the SARS-CoV-2 outbreak, had significantly higher levels of preparedness and awareness (p<0.001). Association rule mining suggests that nurses and doctors who had a ’great-extent-of-confidence’ in handling suspected COVID-19 patients had participated in COVID-19 training courses. Male participants (mean difference = 0.34; 95% CI = 0.22, 0.46; p<0.001) and nurses (mean difference = 0.67; 95% CI = 0.53, 0.81; p<0.001) had higher preparedness scores compared to women participants and doctors. Interpretation There was an unsurprising high level of awareness and preparedness among HCWs who participated in COVID-19 training courses. However, disparity existed along the lines of gender and type of HCW. It is unknown whether the difference in COVID-19 preparedness that we detected early in the pandemic may have translated into disproportionate SARS-CoV-2 burden of disease by gender or HCW type.

Finding Associations in Composite Data Sets

2011 ◽  
Vol 7 (3) ◽  
pp. 1-29 ◽  
Author(s):  
M. Sulaiman Khan ◽  
Maybin Muyeba ◽  
Frans Coenen ◽  
David Reid ◽  
Hissam Tawfik
Keyword(s):  
Association Rule ◽  
Synthetic Data ◽  
Data Sets ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Back Ground ◽  
Fuzzy Association Rule ◽  
Definition Of ◽  
Fuzzy Association Rule Mining ◽  
Composite Data

In this paper, a composite fuzzy association rule mining mechanism (CFARM), directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using “properties” associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets.

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.

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