scholarly journals Pola peminjaman buku di perpustakaan Universitas Syiah Kuala menggunakan Algoritma Eclat

2018 ◽  
Vol 14 (1) ◽  
pp. 35
Author(s):  
Muhammad Subianto ◽  
Fitriana AR ◽  
Meildha Hijriyana P.
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Chemistry Education ◽  
Behavior Pattern ◽  
Research Data ◽  
Frequent Itemset ◽  
Collection Method ◽  
Program Of Study ◽  
Support Count ◽  
Lending Behavior

Introduction. UPT Unsyiah Library is one of the facilities in Syiah Kuala University which provides book lending service to users.The library collects all information and has expanded a big data of book lending.Data Collection Method. This research aims to determine the relevance pattern between the book subject and the borrower's program of study, and to determine the pattern of book borrowing based on books that are often borrowed simultaneously. The pattern can be found using one of the methods of data mining that is the association rules mining with Eclat algorithm. Eclat algorithm uses vertical format of dataset to intersect TID list between items in determining support count so that the process of searching frequent itemset is faster.Analysis Data. There are 122.945 book lending data from 2007 to 2015 used in this study. These data show the borrowers’ behavior pattern of book lending behavior in UPT Library Unsyiah, especially the borrowers who are student of this university. Results and Discussions. The Eclat algorithm produces the most frequent and repeatable pattern of book subjects and program of studies from several years of research data, which are Accounting book subjects with its program of study (S1) and Chemistry book subjects with Chemistry Education program of study (S1).Conclusions. The analysis result for the book subject pattern and program of studies shows that the habit of Unsyiah students in borrowing books from the library is accordingly to their program of studies. As for the patterns between books, Eclat algorithm found linkage between books and most often repeated from several periods of years of research data is the book code of 12311 (Fundamentals of educational evaluation) with 42265 (Introduction to evaluation of education).

scholarly journals Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2706 ◽  
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Behavior Pattern ◽  
Sliding Window ◽  
Feature Point ◽  
Sensor Data ◽  
Identification System ◽  
Trajectory Data ◽  
Ship Navigation ◽  
Online Extraction

Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas–Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.

scholarly journals Association Rule Mining Algorithms for Big Data using RDD-ECLAT Algorithms

2021 ◽  
Author(s):  
Martha ◽  
Ramdas Vankdothu ◽  
Hameed Mohd Abdul ◽  
Rekha Gangula
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
New Paradigm ◽  
Rule Mining ◽  
Data Intensive ◽  
Itemset Mining ◽  
Real World Datasets ◽  
Mining Algorithms

Abstract The revolution in technology for storing and processing big data leads to data intensive computing as a new paradigm. To find the valuable and precise big data knowledge, efficient and scalable data mining techniques are required. In data mining, different techniques are applied depending on the kind of knowledge to be mined. Association rules are generated from the frequent itemsets computed by frequent itemset mining (FIM) algorithms. The problem of designing scalable and efficient frequent itemset mining algorithms on the Spark RDD framework. The research done in this thesis aims to improve the performance (in terms of execution time) of the existing Spark-based frequent itemset mining algorithms and efficiently re-design other frequent itemset mining algorithms on Spark. The particular problem of interest is re-designing the Eclat algorithm in the distributed computing environment of the Spark. The paper proposes and implements a parallel Eclat algorithm using the Spark RDD architecture, dubbed RDD-Eclat. EclatV1 is the earliest version, followed by EclatV2, EclatV3, EclatV4, and EclatV5. Each version is the consequence of a different technique and heuristic being applied to the preceding variant. Following EclatV1, the filtered transaction technique is used, followed by heuristics for equivalence class partitioning in EclatV4 and EclatV5. EclatV2 and EclatV3 are slightly different algorithmically, as are EclatV4 and EclatV5. Experiments on synthetic and real-world datasets.

scholarly journals Mining Frequent Itemsets Over Uncertain Database using Matrix

2020 ◽  
Vol 9 (4) ◽  
pp. 2048-2052
Keyword(s):  
Data Mining ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Computation Complexity ◽  
Support Count ◽  
Frequent Items ◽  
Uncertain Database ◽  
Modern Era ◽  
Mining Frequent Itemsets

In the area of data mining for finding frequent itemset from huge database, there exist a lot of algorithms, out of all Apriori algorithm is the base of all algorithms. In Uapriori algorithm each items existential probability is examined with a given support count, if it is greater or equal then these items are known as frequent items, otherwise these are known as infrequent itemsets. In this paper matrix technology has been introduced over Uapriori algorithm which reduces execution time and computational complexity for finding frequent itemset from uncertain transactional database. In the modern era, volume of data is increasing exponentially and highly optimized algorithm is needed for processing such a large amount of data in less time. The proposed algorithm can be used in the field of data mining for retrieving frequent itemset from a large volume of database by taking very less computation complexity.

scholarly journals Frequent Itemset Mining Based on Development of FP-growth Algorithm and Use MapReduce Technique

2021 ◽  
Vol 28 (1) ◽  
pp. 83-98
Author(s):  
Zakria Mahrousa ◽  
Dima Mufti Alchawafa ◽  
Hasan Kazzaz
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Frequent Itemset ◽  
Frequent Patterns ◽  
Memory Consumption ◽  
Daily Growth ◽  
Big Data Mining ◽  
Multiple Data ◽  
Frequent Item Sets

The Finding of frequent itemset in big data is an important task in data mining and knowledgediscovery. The exponential daily growth of data, called “Big Data”, mining frequent patterns from the hugevolumes of data has many challenges due to memory requirement, multiple data dimensions, heterogeneityof data and so on. The complexities related to mining frequent item-sets from a Big Data can be minimizedby using Modified FP-growth algorithm and parallelizing the mining task with Map Reduce framework inHadoop. In this paper, a modified FP-growth based on directed graph with Hadoop framework will reducethe execution time for the massive database and works efficiently on number of nodes (computers). Thealgorithm was tested, our experimental results demonstrated that the proposed algorithm could scale welland efficiently process large datasets. In addition, it achieves improvement in memory consumption to storefrequent patterns and time complexity.

A Study of Big Data Analytical Frameworks in Research Data Management Using Data Mining Techniques

2021 ◽  
pp. 48-67
Author(s):  
Madhavi Arun Vaidya ◽  
Meghana Sanjeeva
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Data Management ◽  
Large Volume ◽  
Research Data ◽  
Research Data Management ◽  
Visualization Tools ◽  
Using Data ◽  
Analytical Frameworks

Research, which is an integral part of higher education, is undergoing a metamorphosis. Researchers across disciplines are increasingly utilizing electronic tools to collect, analyze, and organize data. This “data deluge” creates a need to develop policies, infrastructures, and services in organisations, with the objective of assisting researchers in creating, collecting, manipulating, analysing, transporting, storing, and preserving datasets. Research is now conducted in the digital realm, with researchers generating and exchanging data among themselves. Research data management in context with library data could also be treated as big data without doubt due its properties of large volume, high velocity, and obvious variety. To sum up, it can be said that big datasets need to be more useful, visible, and accessible. With new and powerful analytics of big data, such as information visualization tools, researchers can look at data in new ways and mine it for information they intend to have.

Research on E-Commerce Intelligence Based on IOT and Big Data

2014 ◽  
Vol 496-500 ◽  
pp. 1889-1894 ◽  
Author(s):  
Zhen Long Peng ◽  
You Lan Huang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Computer Network ◽  
Behavior Pattern ◽  
Deep Mining ◽  
Big Data Mining ◽  
Computing Center ◽  
Personal Behavior ◽  
Consumption Prediction

The computer technology together with network technology, communication technology have built a complex basic platform of computer network and a middle platform network which relate human to human, human to machine and machine to machine. Hundreds of millions of GB data generated from these platforms is stored in Cloud Computing Center. Based on this background, the paper analyzes the historical inevitability of IOT and big data, expounds the concept, process and methods of big data mining, and analyzes the natural relationship between big data mining and business intelligence. Through the deep mining of big data, its an unchangeable trend for us to grasp the user or personal behavior pattern and make marketing decision and overall consumption prediction, and then to achieve a comprehensive and advanced business intelligence.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

PENERAPAN CLUSTERING PADA PENDUDUK YANG MEMPUNYAI KELUHAN KESEHATAN DENGAN DATAMINING K-MEANS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Nurul Rofiqo ◽  
Agus Perdana Windarto ◽  
Dedy Hartama
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Population Level ◽  
Research Data ◽  
Health Complaints ◽  
Public Health Services ◽  
National Statistics ◽  
Central Java ◽  
Indonesian Population ◽  
The Government

This study aims to utilize Clushtering Algorithm in grouping the number of people who have health complaints with the K-means algorithm in Indonesia. The source of this research data was collected based on the documents of the provincial population which had health complaints produced by the National Statistics Agency. The data used in this study are data from 2013-2017 consisting of 34 provinces. The method used in this research is K-means Algorithm. Data will be processed by clushtering in 3 clushter, namely clusther high health complaints, clusther moderate and low health complaints. Centroid data for high population level clusters 37.48, Centroid data for moderate population level clusters 27.08, and Centroid data for low population level clusters 14.89. So that obtained an assessment based on the population index that has health complaints with 7 provinces of high health complaints, namely Central Java, Yogyakarta, Bali, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, Gorontalo, 18 provinces of moderate health complaints, and 9 other provinces including low health complaints. This can be an input to the government to give more attention to residents in each region who have high health complaints through improving public health services so that the Indonesian population becomes healthier without health complaints.Keywords: data mining, health complaints, clustering, K-means, Indonesian residents

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