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A review on big data based parallel and distributed approaches of pattern mining
بررسی رویکردهای موازی و توزیع شده مبتنی بر داده های بزرگ مبتنی بر کاوش الگو-2019 Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data
set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining,
and high utility itemset mining. High utility itemset mining is an emerging data science task, aims to
extract knowledge based on a domain objective. The utility of a pattern shows its effectiveness or benefit
that can be calculated based on user priority and domain-specific understanding. The sequential pattern
mining (SPM) issue is much examined and expanded in various directions. Sequential pattern mining
enumerates sequential patterns in a sequence data collection. Researchers have paid more attention in
recent years to frequent pattern mining over uncertain transaction dataset. In recent years, mining itemsets
in big data have received extensive attention based on the Apache Hadoop and Spark framework.
This paper seeks to give a broad overview of the distinct approaches to pattern mining in the Big Data
domain. Initially, we investigate the problem involved with pattern mining approaches and associated
techniques such as Apache Hadoop, Apache Spark, parallel and distributed processing. Then we examine
major developments in parallel, distributed, and scalable pattern mining, analyze them in the big data
perspective and identify difficulties in designing the algorithms. In particular, we study four varieties
of itemsets mining, i.e., parallel frequent itemsets mining, high utility itemset mining, sequential patterns
mining and frequent itemset mining in uncertain data. This paper concludes with a discussion of open
issues and opportunity. It also provides direction for further enhancement of existing approaches. Keywords: Big data | FIM | HUIM | PSPM | Uncertain data mining |
مقاله انگلیسی |
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Mining temporal characteristics of behaviors from interval events in e-learning
-2018 Much of the work in the data mining community mines temporal knowledge based pri
marily on the frequency of events, e.g., frequent pattern mining, ignoring their duration.
This paper discusses a method that mines big learning data by taking both the frequency
and duration into account. It defines a function for evaluating the importance of events,
summarizing them into big uniform events (BUEs) according to the semantics, and further
segmenting the BUEs using a sliding window to avoid the counting bias issue. The task of
finding temporal characteristics is eventually reduced to mining complex temporally fre
quent patterns and association rules. To validate this method, a series of extensive exper
iments are conducted on both synthetic and real datasets to test the system overhead,
quality of patterns, and model parameters. The results show that our mining framework is
serviceable and can effectively improve the quality of patterns.
Keywords: Temporal data mining ، Temporal characteristics ، Interval events ، E-learning |
مقاله انگلیسی |
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Closed frequent similar pattern mining: Reducing the number of frequent similar patterns without information loss
کاوش الگوی مشابه مکرر بسته: کاهش تعداد الگوهای مکرر مشابه بدون از دست دادن اطلاعات-2018 Frequent pattern mining is considered a key task to discover useful information. Despite the quality of
solutions given by frequent pattern mining algorithms, most of them face the challenge of how to re
duce the number of frequent patterns without information loss. Frequent itemset mining addresses this
problem by discovering a reduced set of frequent itemsets, named closed frequent itemsets, from which the
entire frequent pattern set can be recovered. However, for frequent similar pattern mining, where the num
ber of patterns is even larger than for Frequent itemset mining, this problem has not been addressed yet.
In this paper, we introduce the concept of closed frequent similar pattern mining to discover a reduced set
of frequent similar patterns without information loss. Additionally, a novel closed frequent similar pattern
mining algorithm, named CFSP-Miner, is proposed. The algorithm discovers frequent patterns by travers
ing a tree that contains all the closed frequent similar patterns. To do this efficiently, several lemmas to
prune the search space are introduced and proven. The results show that CFSP-Miner is more efficient
than the state-of-the-art frequent similar pattern mining algorithms, except in cases where the number
of frequent similar patterns and closed frequent similar patterns are almost equal. However, CFSP-Miner is
able to find the closed similar patterns, yielding a reduced size of the discovered frequent similar pattern
set without information loss. Also, CFSP-Miner shows good scalability while maintaining an acceptable
runtime performance.
Keywords: Data mining ، Frequent patterns ، Mixed data ، Similarity functions ، Downward closure |
مقاله انگلیسی |
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Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups
معادله کنتراست ژنتیکی انتزاعی نشان می دهد که ژن های جدید ژن خاص برای زیرگروه های اوتیسم هستند-2018 Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and
gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene
groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this
end, we developed a method to associate gene combinations with groups with shared autism traits, targeting
genetic elements that distinguish patient populations with opposing phenotypes. Our computational method
prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight
potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited
Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating
the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant
combinations are tested for significant subgroup associations. We apply this method by contrasting autism
subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes
to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with sub
groups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups,
but our informatics method can explore other meaningful divisions of autism patients, and can further be applied
to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer’s
disease.
Keywords: Data mining ، Autistic disorder ، Genetics ، Frequent pattern mining |
مقاله انگلیسی |
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استفاده از الگوریتم الگوی تکرار شونده، برای تشخیص جوامع در شبکههای اجتماعی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 28 - تعداد صفحات فایل doc فارسی: 50 اخیراٌ، در وب سایتهای شبکهی اجتماعی شاهد حجمی وسیعی از دادههای متنوع هستیم. تحلیل یک چنین دادههایی منجر به کشف اطلاعات و روابط ناشناخته در این شبکهها گردیده است. شناسایی جوامع، فرآیندی است که به شناسایی گرههای مشابه میپردازد و لذا میتوان آنرا وظیفه ای چالش بر انگیز در حیطهی تحلیل دادههای شبکههای اجتماعی دانست. این علم به طور گسترده در جامعهی شبکههای اجتماعی و آنهم از نظر ساختارهای گراف موجود در این شبکهها مورد مطالعه قرار گرفته است. شبکههای اجتماعی آنلاین و همچنین ساختارهای گراف، شامل اطلاعات کاربردی مفیدی در داخل شبکهها میباشند. استفاده از این اطلاعات میتواند بهبود فرآیند کشف یک جامعه را به همراه داشته باشد. در این مطالعه، روشی را برای کشف یک جامعه ارائه میدهیم. علاوه بر استفاده از ارتباطات بین گرهها به منظور بهبود کیفیت جوامع کشف شده، اطلاعات محتوا را نیز مورد استفاده قرار میدهیم. این روش را میتوان روشی جدید بر مبنای الگوهای تکرار شونده و فعالیتهای کاربران در شبکه و مخصوصاٌ سایتهای شبکههای اجتماعی ای دانست که کاربران یک سری فعالیت سلیقه ای را انجام میدهند. روش پیشنهادی ما دو نقش را ایفا میسازد. در ابتدا بر مبنای فعالیتهای کاربران در شبکه، بعضی از جوامعی که دارای کاربران مشابهی میباشند را کشف میکند و به دنبال آن از روابط اجتماعی استفاده کرده و جوامع بیشتری را کشف میسازد. از مقیاس اف ، به منظور ارزیابی نتایج دو مجموعهی داده ای واقعی استفاده میکنیم (Blogcatalog /Flicker). اثبات خواهیم نمود که روش پیشنهادی میتواند کیفیت کشف جوامع را بهبود دهد.
واژگان کلیدی: شبکههای اجتماعی | تشخیص جامعه | کاوش الگوی تکرار شونده | داده کاوی | تحلیل کلان دادهها |
مقاله ترجمه شده |
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Discovering partial periodic-frequent patterns in a transactional database
کشف الگوهای تناوبی مکرر جزئی در یک پایگاه داده های تراکنشی-2017 Time and frequency are two important dimensions to determine the interestingness of a pattern in a
database. Periodic-frequent patterns are an important class of regularities that exist in a database with
respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on
discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete
cyclic repetitions in a database. However, partial periodic-frequent patterns are more common due to the
imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic
frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the peri
odic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a
database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique
has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial
periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that
the proposed model can discover useful information, and the algorithm is efficient.
Keywords: Data mining | Knowledge discovery in databases | Pattern mining | Partial periodicity | Algorithms |
مقاله انگلیسی |
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A Selective Analysis of Microarray Data Using Association Rule Mining
تجزیه و تحلیل انتخابی داده های میکرو آرایه با استفاده از کاوش قوانین انجمنی-2015 Association analysis plays the vital role in the computational biology. DNA Microarrays allow for the simultaneously monitor of expression levels for thousands of genes or entire genomes. Microarray gene association analysis is exposing the biological relevant association between different genes under different experimental samples. Mining association rules has been applied successfully in various types of data for determining interesting association pattern. Frequent pattern mining is becoming a potential approach in microarray gene expression analysis. In this paper the most relevant mining association rules as well as main issues when discovering efficient and practical method for microarray gene association analysis have been reviewed.© 2015 The Authors. Published by Elsevier B.V.Peer-review under responsibility of organizing committee of the Graph Algorithms, High Performance Implementations and Applications (ICGHIA2014).© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of organizing committee of the Graph Algorithms, High Performance Implementations and Applications (ICGHIA2014)
Data mining | Microarray gene expression data | frequent pattern mining | gene association rules | gene expression analysis |
مقاله انگلیسی |
8 |
An uncertainty-based approach: Frequent itemset mining from uncertain data with different item importance
یک رویکرد مبتنی بر عدم قطعیت: کاوش مجموعه موارد مکرر از داده های غیر قطعی با اهمیت موارد مختلف-2015 Article history:Received 29 January 2015Revised 25 August 2015Accepted 26 August 2015 Available online xxxKeywords:Data mining Existential probabilityFrequent pattern mining Uncertain pattern Weight constraintSince itemset mining was proposed, various approaches have been devised, ranging from processing sim- ple item-based databases to dealing with more complex databases including sequence, utility, or graph information. Especially, in contrast to the mining approaches that process such databases containing exact presence or absence information of items, uncertain pattern mining finds meaningful patterns from uncertain databases with items’ existential probability information. However, traditional uncertain mining methods have a problem in that it cannot apply importance of each item obtained from the real world into the mining process. In this paper, to solve such a problem and perform uncertain itemset mining operations more efficiently, we propose a new uncertain itemset mining algorithm additionally considering importance of items such as weight constraints. In our algorithm, both items’ existential probabilities and weight factors are considered; as a result, we can selectively obtain more meaningful itemsets with high importance and existential probabilities. In addition, the algorithm can operate more quickly with less memory by efficiently reducing the number of calculations causing useless itemset generations. Experimental results in this paper show that the proposed algorithm is more efficient and scalable than state-of-the-art methods.© 2015 Elsevier B.V. All rights reserved.
Keywords: Data mining | Existential probability | Frequent pattern mining | Uncertain pattern | Weight constraint |
مقاله انگلیسی |
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Minimal infrequent pattern based approach for mining outliers in data streams
رویکرد مبتنی بر الگوی کمیاب حداقل برای کاوش نقاط دورافتاده در جریان داده ها-2015 Outlier detection is an important task in data mining which aims at detecting patterns that are unusual in a dataset. Though several techniques are proved to be useful in solving some outlier detection problems, there are certain issues yet to be resolved. Most of the existing methods compute distance of points in full dimensional space to detect outliers. But in high dimensional space, the concept of proximity may not be qualitatively meaningful due to the curse of dimensionality and incurs high computational cost. Moreover, the existing methods focus on discovering outliers but do not provide the interpretability of different subspaces that cause the abnormality. Frequent pattern mining based approaches resolve the aforementioned issues. Recently, infrequent pattern mining has attracted the attention of data mining research community which aims at discovering rare associations and researches in this area motivated to propose a new method to detect outliers in data streams. Infrequent patterns are more interesting than frequent patterns in some domains such as fraudulent credit transactions, anomaly detection, etc. In such applications, mining infrequent patterns facilitates detecting outliers. Minimal infrequent patterns are generators of family of infrequent patterns. In this paper, a novel method is presented to detect outliers by mining minimal infrequent patterns from data streams. Three measures namely Transaction Weight- ing Factor (TWF), Minimal Infrequent Deviation Factor (MIPDF) and Minimal Infrequent Pattern based Outlier Factor (MIFPOF) are defined. An algorithm called Minimal Infrequent Pattern based Outlier Detec- tion (MIFPOD) method is proposed for detecting outliers in data streams based on mined minimal infre- quent patterns. The effectiveness of the proposed method is demonstrated on synthetic dataset obtained from vital dataset collected from body sensors and a publicly available real dataset. The experimental results have shown that the proposed method outperforms the existing methods in detecting outliers.© 2014 Elsevier Ltd. All rights reserved.
Keywords: Minimal infrequent pattern | Outlier detection | Data streams | Data mining |
مقاله انگلیسی |
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Efficient data mining for local binary pattern in texture image analysis
داده کاوی کارآمد برای الگوی باینری محلی در تجزیه و تحلیل بافت تصویر-2015 Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the gray levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relative- ly smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capa- bility of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demon- strated the effectiveness and robustness of our approach to different experimental designs and texture images.Published by Elsevier Ltd.
Keywords: Local binary pattern | Frequent pattern mining | Texture image | Feature selection | Classification |
مقاله انگلیسی |