Utility aware clustering for publishing transactional data

Michael Bewong, Jixue Liu, Lin Liu, Jiuyong Li

Research output: Book chapter/Published conference paperConference paperpeer-review

5 Citations (Scopus)


This work aims to maximise the utility of published data for the partition-based anonymisation of transactional data. We make an observation that, by optimising the clustering i.e. horizontal partitioning, the utility of published data can significantly be improved without affecting the privacy guarantees. We present a new clustering method with a specially designed distance function that considers the effect of sensitive terms in the privacy goal as part of the clustering process. In this way, when the clustering minimises the total intra-cluster distances of the partition, the utility loss is also minimised. We present two algorithms DocClust and DetK for clustering transactions and determining the best number of clusters respectively.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication21st Pacific-Asia Conference, PAKDD 2017 Jeju, South Korea, May 23–26, 2017 Proceedings, Part II
EditorsJinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, Yang-Sae Moon
Place of PublicationCham
PublisherSpringer International Publishing AG
Number of pages14
ISBN (Electronic)9783319575292
ISBN (Print)9783319575285
Publication statusPublished - 2017
EventThe 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017) - Seogwipo KAL Hotel , Jeju, Korea, Republic of
Duration: 23 May 201726 May 2017


ConferenceThe 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017)
Country/TerritoryKorea, Republic of
Internet address

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