Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

2021 ◽  
Vol 169 ◽  
pp. 114530
Author(s):  
Areej Ahmad Abdelaal ◽  
Sa'ed Abed ◽  
Mohammad Al-Shayeji ◽  
Mohammad Allaho
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Mining Algorithms

scholarly journals Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

2014 ◽  
Vol 37 ◽  
pp. 109-116 ◽  
Author(s):  
Shamila Nasreen ◽  
Muhammad Awais Azam ◽  
Khurram Shehzad ◽  
Usman Naeem ◽  
Mustansar Ali Ghazanfar
Keyword(s):  
Data Streams ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Mining Algorithms

scholarly journals Preference-Based Frequent Pattern Mining

2008 ◽  
pp. 1280-1299
Author(s):  
Moonjung Cho ◽  
Jian Pei ◽  
Haixun Wang ◽  
Wei Wang
Keyword(s):  
Data Mining ◽  
General Framework ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Performance Study ◽  
Important Data ◽  
Mining Algorithms ◽  
Extensive Performance

Frequent pattern mining is an important data-mining problem with broad applications. Although there are many in-depth studies on efficient frequent pattern mining algorithms and constraint pushing techniques, the effectiveness of frequent pattern mining remains a serious concern: It is non-trivial and often tricky to specify appropriate support thresholds and proper constraints. In this paper, we propose a novel theme of preference-based frequent pattern mining. A user simply can specify a preference instead of setting detailed parameters in constraints. We identify the problem of preference-based frequent pattern mining and formulate the preferences for mining. We develop an efficient framework to mine frequent patterns with preferences. Interestingly, many preferences can be pushed deep into the mining by properly employing the existing efficient frequent pattern mining techniques. We conduct an extensive performance study to examine our method. The results indicate that preference-based frequent pattern mining is effective and efficient. Furthermore, we extend our discussion from pattern-based frequent pattern mining to preference-based data mining in principle and draw a general framework.

scholarly journals Frequent Pattern Mining over Unstructured Data using Semi-Structured Doc-Model and Pattern Ranking

2020 ◽  
pp. 36-42
Author(s):  
Sudhir Tirumalasetty ◽  
A. Divya ◽  
D. Rahitya Lakshmi ◽  
Ch. Durga Bhavani ◽  
D. Anusha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Unstructured Data ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Innovative Methods ◽  
Mining Algorithms ◽  
Doc Model

Frequent pattern mining is an essential data-mining task, with a goal of discovering knowledge in the form of repeated patterns. Many efficient pattern-mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data we are presented with today, the so-called “Big Data”. Scalable parallel algorithms hold the key to solving the problem in this context. This paper reviews recent advances in parallel frequent pattern mining, analysing them through the Big Data lens. Load balancing and work partitioning are the major challenges to be conquered. These challenges always invoke innovative methods to do, as Big Data evolves with no limits. The biggest challenge than before is conquering unstructured data for finding frequent patterns. To accomplish this Semi Structured Doc-Model and ranking of patterns are used.

scholarly journals Evaluation of Frequent Itemset Mining Algorithms-Apriori and FP Growth

2020 ◽  
Vol 4 (6) ◽  
pp. 1-4
Author(s):  
Jismy Joseph ◽  
Kesavaraj G
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Frequent Itemset Mining ◽  
Frequent Patterns ◽  
Apriori Algorithm ◽  
Itemset Mining ◽  
Mining Algorithms ◽  
Time And Space Complexity

Nowadays the Frequentitemset mining (FIM) is an essential task for retrieving frequently occurring patterns, correlation, events or association in a transactional database. Understanding of such frequent patterns helps to take substantial decisions in decisive situations. Multiple algorithms are proposed for finding such patterns, however the time and space complexity of these algorithms rapidly increases with number of items in a dataset. So it is necessary to analyze the efficiency of these algorithms by using different datasets. The aim of this paper is to evaluate theperformance of frequent itemset mining algorithms, Apriori and Frequent Pattern (FP) growth by comparing their features. This study shows that the FP-growth algorithm is more efficient than the Apriori algorithm for generating rules and frequent pattern mining.

scholarly journals Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1478
Author(s):  
Penugonda Ravikumar ◽  
Palla Likhitha ◽  
Bathala Venus Vikranth Raj ◽  
Rage Uday Kiran ◽  
Yutaka Watanobe ◽  
...  
Keyword(s):  
Real World ◽  
Pattern Mining ◽  
Transportation Network ◽  
Frequent Pattern Mining ◽  
Temporal Databases ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Temporal Database ◽  
First Case

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

Clustering of Time Series Data

2011 ◽  
pp. 258-263
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Series Data ◽  
Science And Engineering ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.

Maintenance of Frequent Patterns

2009 ◽  
pp. 273-293 ◽  
Author(s):  
Mengling Feng ◽  
Jinyan Li ◽  
Guozhu Dong ◽  
Limsoon Wong
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Research Opportunities ◽  
Prefix Tree ◽  
Emerging Trends ◽  
Maintenance Problem

This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and conciserepresentation- based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed.

An Efficient Frequent Patterns Mining Algorithm over Data Streams Based on FPD-Graph

2012 ◽  
Vol 433-440 ◽  
pp. 4457-4462 ◽  
Author(s):  
Jun Shan Tan ◽  
Zhu Fang Kuang ◽  
Guo Gui Yang
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Data Generation ◽  
Experiment Data ◽  
Mining Algorithm ◽  
Head Node

The design of synopses structure is an important issue of frequent patterns mining over data stream. A data stream synopses structure FPD-Graph which is based on directed graph is proposed in this paper. The FPD-Graph contains list head node FPDG-Head and list node FPDG-Node. The operations of FPD-Graph consist of insert operation and deletion operation. A frequent pattern mining algorithm DGFPM based on sliding window over data stream is proposed in this paper. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The DGFPM algorithm not only has high precision for mining frequent patterns, but also has low processing time.

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