با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
Mining direct acyclic graphs to find frequent substructures: An experimental analysis on educational data
استخراج نمودارهای حلقوی مستقیم برای یافتن زیر ساختهای مکرر : تجزیه و تحلیل تجربی بر روی داده های آموزشی-2019 The number of undergraduate students joining universities in Brazil has largely grown in the recent years. However, the number of students who actually graduate remains low. Some studies show that this is due to a phenomenon called retention, consisting of a stu- dent taking more time to graduate than the minimum required by the program, which may lead to late graduation. Hence, identifying retention patterns in an undergraduate program may assist the universities in anticipating the entrance of qualified professionals in the job market, while lessening the students’ dropout rate. Undergraduate programs and grade re- ports can be represented by DAGs, in which each course (as a task to be accomplished by each student) is represented as a vertex, and relations between courses are represented as edges. This article proposes methods for mining DAGs using statistical analysis and Apriori- based concepts, to identify retention patterns in undergraduate programs. This work also presents an experimental analysis using real data from Fluminense Federal University, a Brazilian public higher education institution, for evaluating the methods Keywords: Graph mining | Educational mining | Undergraduate program |
مقاله انگلیسی |
2 |
A disease diagnosis and treatment recommendation system based on big data mining and cloud computing
سیستم تشخیص بیماری و درمان مبتنی بر کاوش داده های بزرگ و محاسبات ابری-2018 It is crucial to provide compatible treatment schemes for a disease according to various
symptoms at different stages. However, most classification methods might be ineffective in
accurately classifying a disease that holds the characteristics of multiple treatment stages,
various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and co
operative actions in disease diagnoses and treatments between different departments and
hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors
might have difficulty in identifying them promptly and accurately. Therefore, to maximize
the utilization of the advanced medical technology of developed hospitals and the rich
medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommen
dation System (DDTRS) is proposed in this paper. First, to effectively identify disease symp
toms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced
for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D
D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm sep
arately. The appropriate diagnosis and treatment schemes are recommended for patients
and inexperienced doctors, even if they are in a limited therapeutic environment. More
over, to reach the goals of high performance and low latency response, we implement
a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experi
mental results demonstrate that the proposed DDTRS realizes disease-symptom clustering
effectively and derives disease treatment recommendations intelligently and accurately.
Keywords: Big data mining ، Cloud computing ، Disease diagnosis and treatment ، Recommendation system |
مقاله انگلیسی |
3 |
Mining maximal frequent patterns in transactional databases and dynamic data streams: A spark-based approach
معادن حداکثر الگوهای مکرر در پایگاه داده های معاملاتی و جریان داده های پویا: رویکرد مبتنی بر جرقه-2018 Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic
data streams (DDSs) is substantially important for business intelligence. MFPs, as the smallest set of patterns, help to reveal customers’ purchase rules and market basket analysis (MBA). Although, numerous studies have been carried out in this area, most of them
extend the main-memory based Apriori or FP-growth algorithms. Therefore, these approaches are not only unscalable but also lack parallelism. Consequently, ever increasing big data sources requirements cannot be met. In addition, mining performance in some
existing approaches degrade drastically due to the presence of null transactions. We, therefore, proposed an efficient way to mining MFPs with Apache Spark to overcome these issues. For the faster computation and efficient utilization of memory, we utilized a prime
number based data transformation technique, in which values of individual transaction
have been preserved. After removing null transactions and infrequent items, the resulting
transformed dataset becomes denser compared to the original distributions. We tested our
proposed algorithms in both real static TDBs and DDSs. Experimental results and performance analysis show that our approach is efficient and scalable to large dataset sizes.
Keywords: Big data ، Transactional databases ، Dynamic data streams ، Null transactions ، Prime number theory ، Data mining ، Apache Spark ، Maximal frequent patterns |
مقاله انگلیسی |
4 |
A disease diagnosis and treatment recommendation system based on big data mining and cloud computing
سیستم تشخیص بیماری و توصیه درمانی بر اساس داده کاوی بزرگ و محاسبات ابری-2018 It is crucial to provide compatible treatment schemes for a disease according to various
symptoms at different stages. However, most classification methods might be ineffective in
accurately classifying a disease that holds the characteristics of multiple treatment stages,
various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and co
operative actions in disease diagnoses and treatments between different departments and
hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors
might have difficulty in identifying them promptly and accurately. Therefore, to maximize
the utilization of the advanced medical technology of developed hospitals and the rich
medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommen
dation System (DDTRS) is proposed in this paper. First, to effectively identify disease symp
toms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced
for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D
D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm sep
arately. The appropriate diagnosis and treatment schemes are recommended for patients
and inexperienced doctors, even if they are in a limited therapeutic environment. More
over, to reach the goals of high performance and low latency response, we implement
a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experi
mental results demonstrate that the proposed DDTRS realizes disease-symptom clustering
effectively and derives disease treatment recommendations intelligently and accurately.
Keywords: Big data mining ، Cloud computing ، Disease diagnosis and treatment ، Recommendation system |
مقاله انگلیسی |
5 |
A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model
یک روش کاوش قانون انجمنی معادلات داده های بزرگ برای پارامترهای ترانسفورماتور قدرت براساس مدل نمودار احتمالاتی-2018 The correlative change analysis of state parameters
can provide powerful technical supports for safe, reliable, and
high-efficient operation of the power transformers. However, the
analysis methods are primarily based on a single or a few state
parameters, and hence the potential failures can hardly be found
and predicted. In this paper, a data-driven method of association
rule mining for transformer state parameters has been proposed
by combining the Apriori algorithm and probabilistic graphical
model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned
cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations.
The result indicates that association rules among the numerical
solutions can be accurately mined. Finally, practical measured
data of five 500 kV transformers is analyzed by the proposed
method. The association rules of various state parameters have
been excavated, and then the mined association rules are used in
modifying the prediction results of single state parameters. The
results indicate that the application of the mined association rules
improves the accuracy of prediction. Therefore, the effectiveness
and feasibility of the proposed method in association rule mining
has been proved
Index Terms: Power transformers, state parameters, association rules, big data, data-driven method, Apriori algorithm, probabilistic graph, state prediction |
مقاله انگلیسی |
6 |
Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data
نسخه های Apriori بر اساس MapReduce برای اگو کاوی مکرر بر روی داده های بزرگ-2018 Pattern mining is one of the most important tasks
to extract meaningful and useful information from raw data.
This task aims to extract item-sets that represent any type of
homogeneity and regularity in data. Although many efficient
algorithms have been developed in this regard, the growing
interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper
is to propose new efficient pattern mining algorithms to work
in big data. To this aim, a series of algorithms based on the
MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be
divided into three main groups. First, two algorithms [Apriori
MapReduce (AprioriMR) and iterative AprioriMR] with no
pruning strategy are proposed, which extract any existing itemset in data. Second, two algorithms (space pruning AprioriMR
and top AprioriMR) that prune the search space by means of
the well-known anti-monotone property are proposed. Finally, a
last algorithm (maximal AprioriMR) is also proposed for mining condensed representations of frequent patterns. To test the
performance of the proposed algorithms, a varied collection of
big data datasets have been considered, comprising up to 3·1018
transactions and more than 5 million of distinct single-items. The
experimental stage includes comparisons against highly efficient
and well-known pattern mining algorithms. Results reveal the
interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm
when dealing with small data.
Index Terms: Big data, Hadoop, MapReduce, pattern mining |
مقاله انگلیسی |
7 |
Efficient algorithms for mining colossal patterns in high dimensional databases
الگوریتم های کارآمد برای کاوش الگوهای عظیم در پایگاه داده های ابعادی بالا-2017 Mining association rules plays an important role in decision support systems. To mine strong association
rules, it is necessary to mine frequent patterns. There are many algorithms that have been developed
to efficiently mine frequent patterns, such as Apriori, Eclat, FP-Growth, PrePost, and FIN. However, these
are only efficient with a small number of items in the database. When a database has a large number
of items (from thousands to hundreds of thousands) but the number of transactions is small, these al
gorithms cannot run when the minimum support threshold is also small (because the search space is
huge). This thus causes the problem of mining colossal patterns in high dimensional databases. In 2012,
Sohrabi and Barforoush proposed the BVBUC algorithm for mining colossal patterns based on a bottom
up scheme. However, this needs more time to check subsets and supersets, because it generates a lot
of candidates and consumes more memory to store these. In this paper we propose new, efficient algo
rithms for mining colossal patterns. Firstly, the CP (Colossal Pattern)-tree is designed. Next, we develop
two theorems to rapidly compute patterns of nodes and prune nodes without the loss of information
in colossal patterns. Based on the CP-tree and these theorems, an algorithm (named CP-Miner) is pro
posed to solve the problem of mining colossal patterns. A sorting strategy for efficiently mining colossal
patterns is thus developed. This strategy helps to reduce the number of significant candidates and the
time needed to check subsets and supersets. The PCP-Miner algorithm, which uses this strategy, is then
proposed, and we also conduct experiments to show the efficiency of these algorithms.
Keywords: Bottom up | Colossal patterns | Data mining | High dimensional databases |
مقاله انگلیسی |
8 |
An algorithm of apriori based on medical big data and cloud computing
یک الگوریتم مبتنی بر apriori بر روی داده های بزرگ پزشکی و محاسبات ابری-2016 With the enormous development in the field of medical industry, the value of medical data is highlighted increasingly. The concept of medical big data has become the study target of experts and scholars at the same time. This paper researches the association rules algorithm in existing medical data mining technology, and improves the Apriori algorithm by introducing of interest degree threshold. Based on the Hadoop platform and cloud computing technology, this paper proposes a new association rule algorithm of medical data mining, combined with the MapReduce, interest measure, confidence coefficient, and support degree. At the end of this paper, a simulation experiment is carried out based on Hadoop platform, which proves the superiority of the improved algorithm.
Keywords: Medical data | Apriori algorithm | Cloud computing | Data mining | Hadoop |
مقاله انگلیسی |
9 |
Improvised Apriori Algorithm Using Frequent Pattern Tree for Real Time Applications in Data Mining
الگوریتم بهبود یافته APRIORI با استفاده از درخت فراوانی الگو برای برنامه های کاربردی زمان واقعی در داده کاوی-2015 Apriori Algorithm is one of the most important algorithm which is used to extract frequent itemsets from large database and get the association rule for discovering the knowledge. It basically requires two important things: minimum support and minimum confidence. First, we check whether the items are greater than or equal to the minimum support and we find the frequent itemsets respectively. Secondly, the minimum confidence constraint is used to form association rules. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time and space for scanning the whole database searching on the frequent itemsets, and present an improvement on Apriori.© 2014 The Authors. Published by Elsevier B.V.Peer-review under responsibility of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014).© 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 International Conference on Information and Communication Technologies (ICICT 2014)
Apriori | Improvised Apriori | Minimum Support | Minimum Confidence | Itemsets | Frequent itemsets | Candidate itemsets | Frequent Pattern tree | Conditional patterns | Time and Space Complexity |
مقاله انگلیسی |
10 |
On Parallelization of the NIS-apriori Algorithm for Data Mining
موازی سازی الگوریتم NIS-APRIORI برای داده کاوی-2015 We have been developing the getRNIA software tool for data mining under uncertain information. The getRNIA software tool is powered by the NIS-Apriori algorithm, which is a variation of the well-known Apriori algorithm. This paper considers the parallelization of the NIS-Apriori algorithm, and implements a part of this algorithm based on the Apache-Spark environment. We especially apply the implemented software to two data sets, the Mammographic data set and the Mushroom data set in order to show the property of the parallelization. Even though this parallelization was not so effective for the Mammographic data set, it was much more effective for the Mushroom data set.∗c 2015 The Authors. Published by Elsevier B.V.Peer-review under responsibility of KES International.© 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 KES International
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data mining | getRNIA | NIS-Apriori | parallelization | rough sets | incomplet information | non-deterministic information
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مقاله انگلیسی |