Comparing the Performance of Frequent Pattern Mining Algorithms

Garg, Kanwal; Kumar, Deepak

doi:10.5120/12129-8502

Cited by 23 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While DL may be more expensive for low k and several thousand intersections (i.e. transactions) that are the typical use cases for OLOGRAM, time costs remain in the order of seconds for all ( 32 ).…”

Section: Resultsmentioning

confidence: 99%

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

Ferré

Capponi

Puthier

2021

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

Most epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual tree-based representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtftk

show abstract

Section: Resultsmentioning

confidence: 99%

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

Ferré

Capponi

Puthier

2021

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

show abstract

“…A FP-tree [18] [19] had dense structure which represents data items in the tree format. In this every transaction from the dataset was read and it mapped with a path in FP-tree [11] [12]. This process continues until all the transactions were read from the dataset.…”

Section: Methodsmentioning

confidence: 99%

“…No candidate key generation was needed for this. FP-Growth [11] is much faster than other algorithms. Optimizations techniques are require more to reduce time.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Improved Association Rule Mining Algorithms for Fast Mining with Efficient Tree Structures on Large Datasets

Naresh,

Suguna

2019

IJEAT

View full text Add to dashboard Cite

ARM is a significant area of knowledge mining which enables association rules which are essential for decision making. Frequent itemset mining has a challenge against large datasets. As going on the dataset size increases the burden and time to discover rules will increase. In this paper the ARM algorithms with tree structures like FP-tree, FIN with POC tree and PPC tree are discussed for reducing overheads and time consuming. These algorithms use highly competent data structures for mining frequent itemsets from the database. FIN uses nodeset a unique and novel data structure to extract frequent itemsets and POC tree to store frequent itemset information. These techniques are extremely helpful in the marketing fields. The proposed and implemented techniques reveal that they have improved about performance by means of time and efficiency

show abstract

“…The rules are discovered in tabular dataset of objects that uses different measures to determine interesting data [73]. In the proposed solution, the Apriori algorithm is used for ARM as it finds complete frequent itemsets [74]. ARM takes two fundamental steps to extract usable ARs [75]:…”

Section: Object-based Association Rulesmentioning

confidence: 99%

Discovering and utilising expert knowledge from security event logs

Khan

Parkinson

2019

Journal of Information Security and Applications

View full text Add to dashboard Cite

Vulnerability assessment and security configuration of computer systems is heavily dependent on human experts, which are widely attributed as being in short supply. This can result in a system being left insecure because of the lack of easily accessible experience and specialist resources. While performing security tasks, human experts often revert to a system's event logs to establish security information (configuration changes, errors, etc.). However, finding and exploiting knowledge from event logs is a challenging and time-consuming task for non-experts. Hence there is a strong need to provide mechanisms to make the process easier for security experts, as well as providing tools for those with significantly less security expertise. In this paper, we present a novel technique to process security event logs of a system that have been evaluated and configured by a security expert, extract key domain knowledge indicative of human decision making, and automatically apply acquired knowledge to previously unseen systems by non-experts to propose security improvements. The proposed solution utilises rule mining algorithms to extract security actions from event log entries. The set of identified rules is represented as a domain action model. The domain model and problem instance generated from a previously unseen system can then be used to produce a plan-of-action, which can be exploited by non-professionals to improve their system's security. Empirical analysis is subsequently performed on 21 event logs, where the acquired domain model and identified plans are discussed in terms of accuracy and performance.

show abstract

Comparing the Performance of Frequent Pattern Mining Algorithms

Cited by 23 publications

References 5 publications

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

Implementation of Improved Association Rule Mining Algorithms for Fast Mining with Efficient Tree Structures on Large Datasets

Discovering and utilising expert knowledge from security event logs

Contact Info

Product

Resources

About