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.

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

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.

scholarly journals BIG DATA MINING FOR INTERESTING PATTERNS WITH MAP REDUCE TECHNIQUE

2017 ◽  
Vol 10 (13) ◽  
pp. 191
Author(s):  
Nikhil Jamdar ◽  
A Vijayalakshmi
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Uncertain Data ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Map Reduce ◽  
Frequent Patterns ◽  
Precise Data ◽  
Big Data Mining ◽  
Transactional Databases

There are many algorithms available in data mining to search interesting patterns from transactional databases of precise data. Frequent pattern mining is a technique to find the frequently occurred items in data mining. Most of the techniques used to find all the interesting patterns from a collection of precise data, where items occurred in each transaction are certainly known to the system. As well as in many real-time applications, users are interested in a tiny portion of large frequent patterns. So the proposed user constrained mining approach, will help to find frequent patterns in which user is interested. This approach will efficiently find user interested frequent patterns by applying user constraints on the collections of uncertain data. The user can specify their own interest in the form of constraints and uses the Map Reduce model to find uncertain frequent pattern that satisfy the user-specified constraints 

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 Frequent Patterns Mining

2020 ◽  
pp. 21-29
Author(s):  
Y. Fakir ◽  
R. Elayachi
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Database Systems ◽  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Large Database ◽  
Large Item ◽  
Time Required ◽  
Remarkable Progress

Frequent pattern mining has been an important subject matter in data mining from many years. A remarkable progress in this field has been made and lots of efficient algorithms have been designed to search frequent patterns in a transactional database. One of the most important technique of datamining is the extraction rule in large database. The time required for generating frequent itemsets plays an important role. This paper provides a comparative study of algorithms Eclat, Apriori and FP-Growth. The performance of these algorithms is compared according to the efficiency of the time and memory usage. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems.

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.

An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

2014 ◽  
Vol 22 (06) ◽  
pp. 879-912 ◽  
Author(s):  
Unil Yun ◽  
Eunchul Yoon
Keyword(s):  
Pattern Mining ◽  
Fault Tolerant ◽  
Frequent Pattern Mining ◽  
Search Space ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Performance Study ◽  
Negative Effects ◽  
Previous Definition ◽  
Definition Of

Based on the frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining have been studied to reduce the search space and discover important patterns. In the previous definition of weighted closed patterns, supports of patterns are only considered to compute the closures of the patterns. It means that the closures of weighted frequent patterns cannot be perfectly checked. Moreover, the usefulness of weighted closed frequent patterns depends on the presence of frequent patterns that have supersets with the exactly same weighted support. However, from the errors such as noise, slight changes in items' supports or weights by them have significantly negative effects on the mining results, which may prevent us from obtaining exact and valid analysis results since the errors can break the original characteristics of items and patterns. In this paper, to solve the above problems, we propose a concept of robust weighted closed frequent pattern mining, and an approximate bound is defined on the basis of the concept, which can relax requirements for precise equality among patterns' weighted supports. Thereafter, we propose a weighted approximate closed frequent pattern mining algorithm which not only considers the two approaches but also suggests fault tolerant pattern mining in the noise constraints. To efficiently mine weighted approximate closed frequent patterns, we suggest pruning and subset checking methods which reduce search space. We also report extensive performance study to demonstrate the effectiveness, efficiency, memory usage, scalability, and quality of patterns in our algorithm.

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