An improved Frequent Pattern Mining in Sustainable Learning Practice using Generalized Association Rules

This research focuses on mining the frequent patterns occurred in the given Datasets by Generalization of Association Rules. Frequent pattern mining is a significant as well as interesting problem in the research filed of Data Mining. Building of frequent pattern tree (FP tree), frequent pattern growth (FP growth) and association rule generation are implemented to find the repeated patterns of data. FP tree Construction Algorithm is mainly used to compact a vast database into a extremely compressed, seems to very tiny data structure; hence eliminates the repeated scanning of database. The role of FP growth algorithm is to discover the frequent patterns with FP tree structure and construct the generalized association rules corresponding to the learning data. FP growth algorithm obtained best results as compared with conventional Apriori algorithm. This research provides some practical real time applications pertaining data mining techniques in the field of learning, education, marketing, finance and so on.

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BIG DATA MINING FOR INTERESTING PATTERNS WITH MAP REDUCE TECHNIQUE

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.19634 ◽

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

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Preference-Based Frequent Pattern Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch073 ◽

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.

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

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206216 ◽

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.

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

Expert Systems with Applications ◽

10.1016/j.eswa.2020.114530 ◽

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

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Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Electronics ◽

10.3390/electronics10121478 ◽

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.

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Clustering of Time Series Data

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch042 ◽

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.

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Maintenance of Frequent Patterns

Post-Mining of Association Rules ◽

10.4018/978-1-60566-404-0.ch014 ◽

2009 ◽

pp. 273-293 ◽

Cited By ~ 1

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.

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Research of Data Graph Mining Based on Telecommunication Customers

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.443.402 ◽

2013 ◽

Vol 443 ◽

pp. 402-406 ◽

Cited By ~ 1

Author(s):

Shang Gao ◽

Mei Mei Li

Keyword(s):

Data Mining ◽

Graph Mining ◽

Pattern Mining ◽

Rapid Development ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Practical Significance ◽

Research Progress ◽

Graph Data ◽

Data Graph

With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4457 ◽

2012 ◽

Vol 433-440 ◽

pp. 4457-4462 ◽

Cited By ~ 1

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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