Discovery of Hidden Correlations in a Local Transaction Database Based on Differences of Correlations

2005 ◽  
pp. 537-548 ◽  
Author(s):  
Tsuyoshi Taniguchi ◽  
Makoto Haraguchi ◽  
Yoshiaki Okubo
Keyword(s):  
Hidden Correlations ◽  
Transaction Database ◽  
Local Transaction

Discovery of hidden correlations in a local transaction database based on differences of correlations

2006 ◽  
Vol 19 (4) ◽  
pp. 419-428 ◽  
Author(s):  
Tsuyoshi Taniguchi ◽  
Makoto Haraguchi
Keyword(s):  
Hidden Correlations ◽  
Transaction Database ◽  
Local Transaction

Hidden Correlations: A Self-Exciting Tale from the FX World

2018 ◽  
Author(s):  
Laura Ballotta ◽  
Alessandro Morico
Keyword(s):  
Hidden Correlations

A Local Transaction Service Model for CORBA Collocated Components

2006 ◽  
Author(s):  
M.Gh. Tajgardoon ◽  
M. Sharifi ◽  
M.H. Tajgardoon
Keyword(s):  
Service Model ◽  
Local Transaction

Fast Algorithms for Temporal Association Rules in a Large Database

2005 ◽  
Vol 277-279 ◽  
pp. 287-292 ◽  
Author(s):  
Lu Na Byon ◽  
Jeong Hye Han
Keyword(s):  
Electronic Commerce ◽  
Association Rules ◽  
Association Rule ◽  
Temporal Association ◽  
Large Database ◽  
Running Time ◽  
Smoothing Filter ◽  
Personalized Services ◽  
Temporal Association Rule ◽  
Transaction Database

As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

scholarly journals ANALISIS POLA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA KOPERASI KARYAWAN YAYASAN ANAKKU

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Arfianto Darmawan ◽  
Titin Kristiana
Keyword(s):  
Data Mining ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Business Unit ◽  
Savings And Loans ◽  
Transaction Data ◽  
Existing Problems ◽  
A Value ◽  
Transaction Database ◽  
Using Data

The Anakku Foundation Cooperative is a multi-business cooperative consisting of shop businesses, savings and loans, and student shuttle services. Every sale of stuff services will be inputted data directly to each business unit. The Anakku Foundation Cooperative still has problems, including store transactions that cannot yet answer what items are often sold, when stock items are still difficult to determine the items that are still available or almost running out. Data mining techniques have been mostly used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about the associations between products from a transaction database. Transaction data on school equipment sales at Cooperative Employees of Anakku Foundation can be reprocessed using Data mining applications so as to produce strong association rules between itemset sales of school supplies so that they can provide recommendations for item alignment and simplify the arrangement or strong item placement related to interdependence. The results are found that the highest value of support and confidence is if buying MUSLIM L1.5P1, so it would buy AL-IZHAR II LOGO with a value of 14.5% support and 79.5% confidence

Big Data Analytics for Intrusion Detection

2020 ◽  
pp. 292-316 ◽  
Author(s):  
Luis Filipe Dias ◽  
Miguel Correia
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Semantic Gap ◽  
Behavioral Analysis ◽  
Event Management ◽  
Security Monitoring ◽  
Hidden Correlations ◽  
Security Information

Intrusion detection has become a problem of big data, with a semantic gap between vast security data sources and real knowledge about threats. The use of machine learning (ML) algorithms on big data has already been successfully applied in other domains. Hence, this approach is promising for dealing with cyber security's big data problem. Rather than relying on human analysts to create signatures or classify huge volumes of data, ML can be used. ML allows the implementation of advanced algorithms to extract information from data using behavioral analysis or to find hidden correlations. However, the adversarial setting and the dynamism of the cyber threat landscape stand as difficult challenges when applying ML. The next generation security information and event management (SIEM) systems should provide security monitoring with the means for automation, orchestration and real-time contextual threat awareness. However, recent research shows that further work is needed to fulfill these requirements. This chapter presents a survey on recent work on big data analytics for intrusion detection.

scholarly journals Hidden correlations in quantum optics and quantum information

2018 ◽  
Vol 1071 ◽  
pp. 012015 ◽  
Author(s):  
Margarita A Man’ko
Keyword(s):  
Quantum Information ◽  
Quantum Optics ◽  
Hidden Correlations
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