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A Model-Based Frequency Constraint for Mining Associations from Transaction Data

Michael Hahsler
2006 Data mining and knowledge discovery  
The constraint utilizes knowledge of the process generating transaction data by applying a simple stochastic mixture model (the NB model) which allows for transaction data's typically highly skewed item  ...  In this paper we develop a novel model-based frequency constraint as an alternative to a single, user-specified minimum support.  ...  Mining associations from transaction data The problem of mining associations and rules from transaction data was introduced by Agrawal et al.  ... 
doi:10.1007/s10618-005-0026-2 fatcat:tuui3jlumrfabjfqgzwastibgu

A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments

Fan Jiang, Carson Leung
2015 Algorithms  
As each item in every transaction in these uncertain big data is associated with an existential probability value expressing the likelihood of that item to be present in a particular transaction, computation  ...  Our algorithm uses the MapReduce model on a cloud environment for effective data analytics on these uncertain big data.  ...  Big Data Mining with the MapReduce Model MapReduce [13] is a high-level programming model for processing vast amounts of data.  ... 
doi:10.3390/a8041175 fatcat:l3nxtydm2ze6jgzymvq6kmevxe

A Novel Approach for Finding Rare Items Based on Multiple Minimum Support Framework

Urvi Bhatt, Pratik Patel
2015 Procedia Computer Science  
Based on the study of relevant data structures of the mining space, this approach utilizes a tree structure to ascertain the rare items.  ...  The Proposed approach makes use of Maximum constraint model for extracting the rare items.  ...  Introduction In order to mine useful information, it is necessary to perform processing on a large amount of data for which Data mining methods are widely used.  ... 
doi:10.1016/j.procs.2015.07.391 fatcat:girjqfag3jgbhlljoatakupjxm

Frequent Pattern Mining and Current State of the Art

Kalli Srinivasa Nageswara Prasad, S. Ramakrishna
2011 International Journal of Computer Applications  
Identifying the association rules in large databases play a key role in data mining.  ...  General Terms Data Mining, Market Basket Analysis, Itemset.  ...  Interesting patterns Constraint-based mining A user may be interested in the patterns satisfying some specified constraint.  ... 
doi:10.5120/3114-4279 fatcat:nza37yy2prft3jrueownimthw4

A Constraint Guided Progressive Sequential Mining Waterfall Model for CRM

Bhawna Mallick, Deepak Garg, P. S. Grover
2014 Journal of Computing and Information Technology  
A novel integrative model, Constraint Guided Progressive Sequential Mining Waterfall (CGP-SMW) for knowledge discovery process is proposed.  ...  However, the traditional data mining techniques have no relevant mechanism to provide guidance for business understanding, model selection and dynamic changes made in the databases.  ...  Acknowledgements The authors would like to thank the Editor and anonymous referees for providing valuable comments and constructive suggestions.  ... 
doi:10.2498/cit.1002134 fatcat:s52c62rmxrhozhrhamovgwcpci

Step-by-Step Model for the Study of the Apriori Algorithm for Predictive Analysis

Daniel Grigore ROŞCA, Dumitru RĂDOIU
2015 Scientific Bulletin of the ''Petru Maior" University of Tîrgu Mureș  
process of data mining of a test transactional data base.  ...  variables) or size of the transactional data-base.  ...  Introduction Data mining is a business intelligence process based on extracting new data (e.g. hidden patterns) from available data sets [4] .  ... 
doaj:002e4f36b86a4267acef78c6e1e16c4f fatcat:33x57apzajgz7h6dykbs5cfhpu

Mining Rare Association Rules in the Datasets with Widely Varying Items' Frequencies [chapter]

R. Uday Kiran, P. Krishna Reddy
2010 Lecture Notes in Computer Science  
However, that model still extracts uninteresting rules if the items' frequencies in a dataset vary widely.  ...  It is difficult to mine rare association rules with a single minimum support (minsup) constraint because low minsup can result in generating too many rules in which some of them can be uninteresting.  ...  Introduction Association rule mining is an important data mining technique which discovers interesting associations among the entities (or items) in a dataset.  ... 
doi:10.1007/978-3-642-12026-8_6 fatcat:76p6tiwbwffelbcsf62axrnehi

Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach [chapter]

Jean-François Boulicaut
2004 Lecture Notes in Computer Science  
Mining itemsets has been proved useful not only for association rule mining but also feature construction, classification, clustering, etc.  ...  With an IDB the user/analyst performs a set of very different operations on data using a query language, powerful enough to perform all the required elaborations, such as data preprocessing, pattern discovery  ...  A simple model has been introduced in [55] that considers a data mining process as a sequence of queries over the data but also the so-called theory of the data.  ... 
doi:10.1007/978-3-540-44497-8_1 fatcat:eidindj7urhx5ij477velnmpze

An Improved Recognition Method of Weighted Rules and Its Application in Recommendation Algorithm

2022 Academic Journal of Computing & Information Science  
The weighted temporal association rules mined by the effective frequency length weighted association rules mining algorithm can improve the accuracy of recommendation, and the accuracy of recommendation  ...  The method has made great improvement in three aspects of association rule design, which are effective length recognition of recommendation rule, weighted association rule mining combined with frequency  ...  [10] proposed an efficient method for mining weighted association rules from weighted transaction databases, using the unresolvable matrix to quickly find all nodes of the lattice.  ... 
doi:10.25236/ajcis.2022.050102 fatcat:gmrhlrbu5fhb5hh457bgeii3qu

Discovering Knowledge using a Constraint-based Language [article]

Patrice Boizumault, Bruno Crémilleux, Mehdi Khiari, Samir Loudni,, Jean-Philippe Métivier
2011 arXiv   pre-print
In parallel, recent works investigating relationships between data mining and constraint programming (CP) show that the CP paradigm is a nice framework to model and mine such patterns in a declarative  ...  The usefulness of such a declarative approach is highlighted by several examples coming from the clustering based on associations. This language has been implemented in the CP framework.  ...  As future work, we intend to enrich our constraint-based language with further constraints to capture and model a wide range of data mining tasks.  ... 
arXiv:1107.3407v1 fatcat:cdhzqrjlbbbini6p5pfmeog4ii

Survey On Utility Data Mining

M Ganesan, S Shankar
2017 Zenodo  
Association Rule Mining focuses on existence of an item in a transaction, whether or not it is purchased.  ...  Traditional data mining methodologies focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases.  ...  Yao [4] defines the utility mining problem as one of the cases of constraint mining. This work  ... 
doi:10.5281/zenodo.813941 fatcat:boaxxi6k2vf2pei4epaplyuclm

Generalized Affinity-Based Association Rule Mining for Multimedia Database Queries

Mei-Ling Shyu, Shu-Ching Chen, R. L. Kashyap
2001 Knowledge and Information Systems  
In response to such a demand, association rule mining has emerged and proven to be a highly successful technique for discovering knowledge from large databases.  ...  In this paper, we explore a generalized affinity-based association rule mining approach to discover the quasi-equivalence relationships from a network of databases.  ...  For this purpose, a generalized affinity-based association rule mining approach that discovers the set of quasi-equivalent media objects from databases is proposed.  ... 
doi:10.1007/pl00011671 fatcat:6gbdnq64drdglcov23nx6jegwi

Mining Frequent Patterns in Large Scale Databases Using Adaptive FP-Growth Approach

Doo Hee Han, Zhang Nv
2017 Bonfring International Journal of Industrial Engineering and Management Science  
Frequent Patterns (FPs) are extremely vital in knowledge discovery and data mining process, for instance, mining of association rules, correlations etc.  ...  Mining association rules in the midst of items in a large database is one of the most vital data mining problems.  ...  We propose in this paper a method to mine frequent from a large data without transposing the data set.  ... 
doi:10.9756/bijiems.8326 fatcat:dmio4w7inrdpfaeagtrhzson24

Application of Transaction Mining Based on FP-Table Algorithm in Mobile Electricity Market

Chuncheng Gao, Yong Dai, Minghai Jiao
2015 Open Journal of Social Sciences  
For the massive and various trading data, transaction mining algorithm is very useful to find the relationship of correlative elements such as trade price and power capacity, and it always occurs between  ...  Electricity market trade based on mobile intelligent device will extend the volume of transaction.  ...  Then the transaction mining application effect will feed back the experiment in the paper.  ... 
doi:10.4236/jss.2015.37014 fatcat:mbqzuah7fvgrznkb44micwxy7m

Mining related queries from Web search engine query logs using an improved association rule mining model

Xiaodong Shi, Christopher C. Yang
2007 Journal of the American Society for Information Science and Technology  
Unlike many other rival techniques, it also performs reasonably well on less frequent input queries. extracted user sessions into query transactions, and (3) mining related queries from query transactions  ...  The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules.  ...  Acknowledgment This project was supported by the Earmarked Grant for Research from the Hong Kong Research Grant Council, 4178/05E  ... 
doi:10.1002/asi.20632 fatcat:bjhzxveuwvdrzdkqllkehcf6om
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