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

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

Procedia Computer Science ◽

10.1016/j.procs.2014.08.019 ◽

2014 ◽

Vol 37 ◽

pp. 109-116 ◽

Cited By ~ 21

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

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Distributed frequent hierarchical pattern mining for robust and efficient large-scale association discovery

10.32469/10355/63867 ◽

2017 ◽

Author(s):

◽

Michael Phinney

Keyword(s):

Data Mining ◽

Distributed Computing ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Generation Process ◽

Computing Environment ◽

Wide Range ◽

Mining Algorithms ◽

Hierarchical Pattern

Frequent pattern mining is a classic data mining technique, generally applicable to a wide range of application domains, and a mature area of research. The fundamental challenge arises from the combinatorial nature of frequent itemsets, scaling exponentially with respect to the number of unique items. Apriori-based and FPTree-based algorithms have dominated the space thus far. Initial phases of this research relied on the Apriori algorithm and utilized a distributed computing environment; we proposed the Cartesian Scheduler to manage Apriori's candidate generation process. To address the limitation of bottom-up frequent pattern mining algorithms such as Apriori and FPGrowth, we propose the Frequent Hierarchical Pattern Tree (FHPTree): a tree structure and new frequent pattern mining paradigm. The classic problem is redefined as frequent hierarchical pattern mining where the goal is to detect frequent maximal pattern covers. Under the proposed paradigm, compressed representations of maximal patterns are mined using a top-down FHPTree traversal, FHPGrowth, which detects large patterns before their subsets, thus yielding significant reductions in computation time. The FHPTree memory footprint is small; the number of nodes in the structure scales linearly with respect to the number of unique items. Additionally, the FHPTree serves as a persistent, dynamic data structure to index frequent patterns and enable efficient searches. When the search space is exponential, efficient targeted mining capabilities are paramount; this is one of the key contributions of the FHPTree. This dissertation will demonstrate the performance of FHPGrowth, achieving a 300x speed up over state-of-the-art maximal pattern mining algorithms and approximately a 2400x speedup when utilizing FHPGrowth in a distributed computing environment. In addition, we allude to future research opportunities, and suggest various modifications to further optimize the FHPTree and FHPGrowth. Moreover, the methods we offer will have an impact on other data mining research areas including contrast set mining as well as spatial and temporal mining.

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Comparative Study of Frequent Pattern Mining Techniques

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.1022 ◽

2011 ◽

Vol 403-408 ◽

pp. 1022-1027 ◽

Cited By ~ 1

Author(s):

Gauravjeet Singh ◽

Sandeep Bal ◽

Poonamjeet Kaur ◽

Kanwaljit Kaur

Keyword(s):

Data Mining ◽

Comparative Study ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Mining Algorithms

Frequent pattern mining has been a focused theme in data mining research. Lots of techniques have been proposed to improve the performance of frequent pattern mining algorithms. This paper presents review of different frequent mining techniques. With each technique, we have provided brief description of the technique. At the end, we compared different frequent pattern mining techniques.

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Evaluation of Frequent Itemset Mining Algorithms-Apriori and FP Growth

International Journal of Engineering Technology and Management Sciences ◽

10.46647/ijetms.2020.v04i06.001 ◽

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.

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1118.1292s219 ◽

2019 ◽

Vol 9 (2S2) ◽

pp. 776-780

Keyword(s):

Data Mining ◽

Association Rules ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Frequent Patterns ◽

Tree Construction ◽

Generalized Association Rules ◽

Education Marketing ◽

The Given

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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Clustering of Time Series Data

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch033 ◽

2011 ◽

pp. 172-175

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, which 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.

Download Full-text