scholarly journals ASSOCIATION RULE MINING FOR GENE EXPRESSION DATA

2014 ◽  
pp. 240-243
Author(s):  
O. V. KALE ◽  
B. F. MOMIN
Keyword(s):  
Microarray Data ◽  
Association Rule ◽  
Search Space ◽  
Biological Research ◽  
Research Association ◽  
Expression Data ◽  
Rule Mining ◽  
Confidence Measure ◽  
Data Set ◽  
Space Experiments

Microarray technology has created a revolution in the field of biological research. Association rules can not only group the similarly expressed genes but also discern relationships among genes. We propose a new row-enumeration rule mining method to mine high confidence rules from microarray data. It is a support-free algorithm that directly uses the confidence measure to effectively prune the search space. Experiments on Leukemia microarray data set show that proposed algorithm outperforms support-based rule mining with respect to scalability and rule extraction.

scholarly journals Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining

2021 ◽  
Vol 3 ◽  
Author(s):  
Oliver Haas ◽  
Luis Ignacio Lopera Gonzalez ◽  
Sonja Hofmann ◽  
Christoph Ostgathe ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Palliative Care ◽  
Association Rule ◽  
Association Rule Mining ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Rule Mining ◽  
Data Set ◽  
Routinely Collected Data ◽  
Previous State ◽  
Insight Into

We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.

Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 18-37
Author(s):  
Mehmet Bicer ◽  
Daniel Indictor ◽  
Ryan Yang ◽  
Xiaowen Zhang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Search Space ◽  
Frequent Itemset ◽  
Incremental Algorithm ◽  
Frequent Patterns ◽  
Lazy Evaluation ◽  
Rule Mining ◽  
Implementation Techniques ◽  
Common Technique

Association rule mining is a common technique used in discovering interesting frequent patterns in data acquired in various application domains. The search space combinatorically explodes as the size of the data increases. Furthermore, the introduction of new data can invalidate old frequent patterns and introduce new ones. Hence, while finding the association rules efficiently is an important problem, maintaining and updating them is also crucial. Several algorithms have been introduced to find the association rules efficiently. One of them is Apriori. There are also algorithms written to update or maintain the existing association rules. Update with early pruning (UWEP) is one such algorithm. In this paper, the authors propose that in certain conditions it is preferable to use an incremental algorithm as opposed to the classic Apriori algorithm. They also propose new implementation techniques and improvements to the original UWEP paper in an algorithm we call UWEP2. These include the use of memorization and lazy evaluation to reduce scans of the dataset.

scholarly journals Data Mining Application using Association Rule Mining ECLAT Algorithm Based on SPMF

2018 ◽  
Vol 164 ◽  
pp. 01019
Author(s):  
Jason Reynaldo ◽  
David Boy Tonara
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Data Storage ◽  
Association Rule ◽  
Association Rule Mining ◽  
Search Space ◽  
Rule Mining ◽  
Master Data ◽  
Research Domain ◽  
Data Mining Application

Data mining is an important research domain that currently focused on knowledge discovery database. Where data from the database are mined so that information can be generated and used effectively and efficiently by humans. Mining can be applied to the market analysis. Association Rule Mining (ARM) has become the core of data mining. The search space is exponential in the number of database attributes and with millions of database objects the problem of I/O minimization becomes paramount. To get the information and the data such as, observation of the master data storage systems and interviews were done. Then, ECLAT algorithm is applied to the open-source library SPMF. In this project, this application can perform data mining assisted by open source SPMF with determined writing format of transaction data. It successfully displayed data with 100 % success rate. The application can generate a new easier knowledge which can be used for marketing the product.

An extended association rule mining strategy for gene relationship discovery from microarray data

2012 ◽  
Vol 84 (2) ◽  
pp. 384-396 ◽  
Author(s):  
Bin Peng ◽  
Dianwen Zhu ◽  
Xiaowei Yang ◽  
Ling Liu ◽  
Wenquan Huang ◽  
...  
Keyword(s):  
Microarray Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

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 Boosting association rule mining in large datasets via Gibbs sampling

2016 ◽  
Vol 113 (18) ◽  
pp. 4958-4963 ◽  
Author(s):  
Guoqi Qian ◽  
Calyampudi Radhakrishna Rao ◽  
Xiaoying Sun ◽  
Yuehua Wu
Keyword(s):  
Gibbs Sampling ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
General Rule ◽  
Search Space ◽  
Stochastic Search ◽  
Importance Measure ◽  
Rule Mining ◽  
Ergodic Markov Chain

Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this paper, we develop a Gibbs-sampling–induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. Also a general rule importance measure is proposed to direct the stochastic search so that, as a result of the randomly generated association rules constituting an ergodic Markov chain, the overall most important rules in the itemset space can be uncovered from the reduced dataset with probability 1 in the limit. In the simulation study and a real genomic data example, we show how to boost association rule mining by an integrated use of the stochastic search and the Apriori algorithm.

An Efficient Approach for Incremental Association Rule Mining through Histogram Matching Technique

2012 ◽  
Vol 2 (2) ◽  
pp. 29-42 ◽  
Author(s):  
Ajay Kumar ◽  
Shishir Kumar ◽  
Sakshi Saxena
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Association Rule ◽  
Clustering Algorithm ◽  
Synthetic Data ◽  
Rule Mining ◽  
Data Set ◽  
Matching Technique ◽  
Burst Data

The objective of the work being presented is to propose an approach for obtaining appropriate association rules when the data set is being incrementally updated. During this process raw data is clustered by K-mean Clustering Algorithm and appropriate rules are generated for each cluster. Further, a histogram and probability density function are also generated for each cluster. When Burst data set is coming to the system, initially the histogram and probability density function of this new data set are obtained. The new data set has to be added to the cluster whose histogram and probability density functions are almost similar. The proposed method is evaluated and explained on synthetic data.

Defining the Factors that Effect User Interest on Social Network News Feeds via Fuzzy Association Rule Mining

2013 ◽  
pp. 334-345
Author(s):  
Basar Öztaysi ◽  
Sezi Çevik Onar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Data Set ◽  
Frequent Items ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining

Social networking became one of the main marketing tools in the recent years since it’s a faster and cheaper way to reach the customers. Companies can use social networks for efficient communication with their current and potential customers but the value created through the usage of social networks depends on how well the organizations use these tools. Therefore a support system which will enhance the usage of these tools is necessary. Fuzzy Association rule mining (FARM) is a commonly used data mining technique which focuses on discovering the frequent items and association rules in a data set and can be a powerful tool for enhancing the usage of social networks. Therefore the aim of the chapter is to propose a fuzzy association rule mining based methodology which will present the potential of using the FARM techniques in the field of social network analysis. In order to reveal the applicability, an experimental evaluation of the proposed methodology in a sports portal will be presented.

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