From Change Mining to Relevance Feedback

2009 ◽  
pp. 12-37 ◽  
Author(s):  
Mirko Boettcher ◽  
Georg Ruß ◽  
Detlef Nauck ◽  
Rudolf Kruse
Keyword(s):  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Novel Approach ◽  
Large Numbers ◽  
Textual Description ◽  
Rule Change ◽  
Mining Algorithms

Association rule mining typically produces large numbers of rules, thereby creating a second-order data mining problem: which of the generated rules are the most interesting? And: should interestingness be measured objectively or subjectively? To tackle the amount of rules that are created during the mining step, the authors propose the combination of two novel ideas: first, there is rule change mining, which is a novel extension to standard association rule mining which generates potentially interesting time-dependent features for an association rule. It does not require changes in the existing rule mining algorithms and can therefore be applied during post-mining of association rules. Second, the authors make use of the existing textual description of a rule and those newly derived objective features and combine them with a novel approach towards subjective interestingness by using relevance feedback methods from information retrieval. The combination of these two new approaches yields a powerful, intuitive way of exploring the typically vast set of association rules. It is able to combine objective and subjective measures of interestingness and will incorporate user feedback. Hence, it increases the probability of finding the most interesting rules given a large set of association rules.

scholarly journals Interactive Content Based Image Retrieval using Multiuser Feedback

2017 ◽  
Vol 1 (4) ◽  
pp. 165
Author(s):  
M. Premkumar ◽  
R. Sowmya
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Human Perception ◽  
Content Based Image Retrieval ◽  
Rule Mining ◽  
Query Image ◽  
Feedback Techniques

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

A Survey on Fuzzy Association Rule Mining

2013 ◽  
Vol 9 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Harihar Kalia ◽  
Satchidananda Dehuri ◽  
Ashish Ghosh
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Quantitative Data ◽  
Association Rule Mining ◽  
Real Life ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Fuzzy Association Rule ◽  
Mining Algorithms ◽  
Fuzzy Association Rule Mining

Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

A New Algorithm of Association Rules Mining Based on Relation Matrix

2011 ◽  
Vol 179-180 ◽  
pp. 55-59
Author(s):  
Ping Shui Wang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Automatic Extraction ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Research Areas ◽  
Mining Algorithms ◽  
Relation Matrix

Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Finding association rules can be derived based on mining large frequent candidate sets. Aiming at the poor efficiency of the classical Apriori algorithm which frequently scans the business database, studying the existing association rules mining algorithms, we proposed a new algorithm of association rules mining based on relation matrix. Theoretical analysis and experimental results show that the proposed algorithm is efficient and practical.

Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

2019 ◽  
Vol 18 (03) ◽  
pp. 1950028
Author(s):  
Sheel Shalini ◽  
Kanhaiya Lal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Search Space ◽  
Temporal Association ◽  
Incremental Mining ◽  
Rule Mining ◽  
Novel Approach ◽  
Temporal Association Rule ◽  
Mining Algorithms

Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

scholarly journals EARMGA and Apriori Algorithm's Performance Evaluation for Association Rule Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 6982-6987
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Large Databases ◽  
Algorithm Comparison ◽  
Average Confidence ◽  
Mining Algorithms

Association rule mining techniques are important part of data mining to derive relationship between attributes of large databases. Association related rule mining have evolved huge interest among researchers as many challenging problems can be solved using them. Numerous algorithms have been discovered for deriving association rules effectively. It has been evaluated that not all algorithms can give similar results in all scenarios, so decoding these merits becomes important. In this paper two association rule mining algorithms were analyzed, one is popular Apriori algorithm and the other is EARMGA (Evolutionary Association Rules Mining with Genetic Algorithm). Comparison of these two algorithms were experimentally performed based on different datasets and different parameters like Number of rules generated, Average support, Average Confidence, Covered records were detailed.

scholarly journals Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 309-316
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Healthcare Data ◽  
Data Mining Approach ◽  
Mining Community ◽  
Pattern Growth ◽  
Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

Effective Mining of Weighted Fuzzy Association Rules

2010 ◽  
pp. 47-64 ◽  
Author(s):  
Maybin Muyeba ◽  
M. Sulaiman Khan ◽  
Frans Coenen
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Data ◽  
Closure Property ◽  
Data Sets ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Novel Approach ◽  
Downward Closure

A novel approach is presented for effectively mining weighted fuzzy association rules (ARs). The authors address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance wrt some user defined criteria. Most works on weighted association rule mining do not address the downward closure property while some make assumptions to validate the property. This chapter generalizes the weighted association rule mining problem with binary and fuzzy attributes with weighted settings. Their methodology follows an Apriori approach but employs T-tree data structure to improve efficiency of counting itemsets. The authors’ approach avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. The chapter presents experimental results on both synthetic and real-data sets and a discussion on evaluating the proposed approach.

A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies

2019 ◽  
Vol 14 (1) ◽  
pp. 112-131
Author(s):  
Emad Alsukhni ◽  
Ahmed AlEroud ◽  
Ahmad A. Saifan
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Sets ◽  
Rule Mining ◽  
Useful Knowledge ◽  
Novel Association ◽  
Rule Pruning ◽  
Occurrence Patterns ◽  
Mining Algorithms

Association rule mining is a very useful knowledge discovery technique to identify co-occurrence patterns in transactional data sets. In this article, the authors proposed an ontology-based framework to discover multi-dimensional association rules at different levels of a given ontology on user defined pre-processing constraints which may be identified using, 1) a hierarchy discovered in datasets; 2) the dimensions of those datasets; or 3) the features of each dimension. The proposed framework has post-processing constraints to drill down or roll up based on the rule level, making it possible to check the validity of the discovered rules in terms of support and confidence rule validity measures without re-applying association rule mining algorithms. The authors conducted several preliminary experiments to test the framework using the Titanic dataset by identifying the association rules after pre- and post-constraints are applied. The results have shown that the framework can be practically applied for rule pruning and discovering novel association rules.

Association Rule Mining Based on Multidimensional Pattern Relations

2014 ◽  
Vol 918 ◽  
pp. 243-245
Author(s):  
Yu Ke Chen ◽  
Tai Xiang Zhao
Keyword(s):  
Decision Support ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Multidimensional Data ◽  
Incremental Mining ◽  
Rule Mining ◽  
Online Mining ◽  
Mining Algorithms

Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide adhoc, query driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis.

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