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Detecting illicit transactions in bitcoin: a wavelet-temporal graph transformer approach for anti-money laundering. [PDF]

Sci Rep
Lin Z, Luo Q, Wu D, Shen J, Li L, Nong X, Qin Z.   +6 more
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Accessing multiversion data in database transactions

, 2010
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Outlier detection in transactional data

Intelligent Data Analysis, 2010
Outlier detection is studied in the context of supervised (with class label) and unsupervised (without class label – e.g., clustering) data. To the best of our knowledge there has been no study on outlier detection in transactional database, e.g. market basket data where each transaction has a number of items and the number of items in each transaction
Manoranjan Dash, Willie Ng
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Mobile data and transaction management

Information Sciences, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sanjay Kumar Madria, Mukesh K. Mohania, Sourav S. Bhowmick, Bharat K. Bhargava   +3 more
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Simulation of Bank Transaction Data

2019
When working with data such as financial transactions or user activity logs, in domains with inherent privacy concerns, you will certainly run into problems with data protection and data availability. Among possible approaches to cope with these problems are data anonymization and data simulation. One of essential advantages in favor of data simulation
Martin Mocko, Jakub Sevcech
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Interactive Clustering for Transaction Data

2001
We propose a clustering algorithm, OAK, targeted to transaction data as typified by market basket data, web documents, and categorical data. OAK is interactive, incremental, and scalable. Use of a dendrogram facilitates the dynamic modification of the number of clusters.
Yongqiao Xiao, Margaret H. Dunham
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Contextual Analysis of Transactional Data

2019
Data analysis is often attempted by business stakeholder on transactional databases, that are maintained within the overall enterprise system, in order to understand patterns of user behavior, transaction types, and their impact on the utility and value of interest to the enterprise.
Vangalur S. Alagar, Kaiyu Wan
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Clustering Transactional Data Streams

2006
The challenge of mining data streams is three fold. Firstly, an algorithm for a particular data mining task is subject to the sequential one-pass constraint; secondly, it must work under bounded resources such as memory and disk space; thirdly, it should have capabilities to answer time-sensitive queries. Dealing with transactional data streams is even
Yanrong Li, Raj P. Gopalan
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Identifying Relationships in Transactional Data

2012
Association rules is the traditional way used to study market basket or transactional data. One drawback of this analysis is the huge number of rules generated. As a complement to association rules, Association Rules Network (ARN), based on Social Network Analysis (SNA) has been proposed by several researchers.
Melissa Rodrigues, João Gama 0001, Carlos Abreu Ferreira   +2 more
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Nonsensitive Data and Approximate Transactions

IEEE Transactions on Software Engineering, 1983
A methodology has been proposed for solving database problems requiring only approximate solutions. Data items are classified as sensitive and nonsensitive. An approximate transaction modifies only the nonsensitive data items which need not satisfy strong consistency constraints, and provides results only up to a degree of approximation. Further, it is
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