CApriori: Conviction based Apriori algorithm for discovering frequent determinant patterns from high dimensional datasets

Prasanna, K.; Seetha, M.; Kumar, Abhishek

doi:10.1109/icsemr.2014.7043622

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…It may increase some other performance metrics like memory usage. Prasanna, Seetha, & Kumar, 2014 noted that the memory overhead of parallel processing could have a negative impact on system performance (Hong & Bian, 2008). One of the solutions that they proposed is to employ a memory aware resource allocation policy.…”

Section: Parallel Processingmentioning

confidence: 99%

A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

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Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Section: Parallel Processingmentioning

confidence: 99%

A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

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“…The execution time of the technique is very effective. The parameter k is considered as the input so as to maintain maximum similarity with intra cluster, likewise minimum similarity with inter cluster [21]. Consequently, the k clusters to a separate group of n data objects.…”

Section: Estimating Software Bug Complexity Using K-meansmentioning

confidence: 99%

Assessment of Software Bug Complexity and Severity using Evolutionary SOM Scheme

Rao¹,

Babu²

2019

IJEAT

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The software defect prediction and assessment plays a significant role in the software development process. Predicting software defects in the earlier stages will increases the software quality, reliability and efficiency, the cost of detecting and eliminating software defects have been the most expensive task during both development and maintenance process, as software demands increase and delivery of the software span decreased, ensuring software quality becomes a challenge. However, due to inadequate testing, no software can pretend to be free from errors. Bug repositories are used for storing and managing bugs in software projects. A bug in the repositories is recorded as a bug report. When a bug is found by a tester its available information is entered in defect tracking systems. During its resolution process a bug enters into various bug states. These defect tracking systems enable user to give the information about the bugs while running the software. However, the severity prediction has recently gained a lot of attention in software maintenance. Bugs with greater severity should be resolved before bugs with lower severity. In this paper an evolutionary interactive scheme to evaluate bug reports and assess the severity is proposed. This paper presents a Software Bug Complexity Cluster (SBCC) using Self Organizing Maps. In this SBCC a feature matrix is built using bug durations and the complexities of software bugs are categorized into distinct clusters including Blocker, Critical, Major, Trivial and Minor by specifying negative impact of the defect using two different techniques, namely k-means and SOM. Bug duration, proximity error and pre-defined distance functions are used to estimate the accuracy of different bug complexities. Our systematic study found that SOM's proximity error and fitness have greater performance and efficiency than K-Means. The collected results showed better performance for the SBCC with respect to fitness and cluster proximity error.

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“…The unsupervised learning techniques for clustering are routinely used in transcriptomics. The clustering analysis is applied for the study of expression relationships between genes ( Liu, Cheng and Tseng, 2011 ), extracting biologically relevant expression features ( Kong et al, 2008 ), discovering frequent determinant patterns ( Prasanna, Seetha and Kumar, 2014 ), etc.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive survey on computational learning methods for analysis of gene expression data

Bhandari

Walambe

Kotecha

et al. 2022

Front. Mol. Biosci.

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Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

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CApriori: Conviction based Apriori algorithm for discovering frequent determinant patterns from high dimensional datasets

Cited by 10 publications

References 10 publications

A survey on association rules mining using heuristics

A survey on association rules mining using heuristics

Assessment of Software Bug Complexity and Severity using Evolutionary SOM Scheme

A comprehensive survey on computational learning methods for analysis of gene expression data

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