Generating insights through data preparation, visualization, and analysis: Framework for combining clustering and data visualization techniques for low-cardinality sequential data

Svetlozar Nestorov; Boris Jukić; Nenad Jukić; Abhishek Sharma; Sippo Rossi; Nenad Jukic

doi:10.1016/j.dss.2019.113119

Generating insights through data preparation, visualization, and analysis: Framework for combining clustering and data visualization techniques for low-cardinality sequential data

Svetlozar Nestorov
, Boris Jukić
, Nenad Jukić
, Abhishek Sharma
, Sippo Rossi
, Nenad Jukic

Loyola University Chicago
Clarkson University
Aalto University

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Decision Support Systems
Volume	125
DOIs	https://doi.org/10.1016/j.dss.2019.113119
State	Published - Oct 1 2019

Disciplines

Computer Sciences

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10.1016/j.dss.2019.113119License: Unspecified

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title = "Generating insights through data preparation, visualization, and analysis: Framework for combining clustering and data visualization techniques for low-cardinality sequential data",

author = "Svetlozar Nestorov and Boris Juki{\'c} and Nenad Juki{\'c} and Abhishek Sharma and Sippo Rossi and Nenad Jukic",

year = "2019",

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doi = "10.1016/j.dss.2019.113119",

language = "American English",

volume = "125",

journal = "Decision Support Systems",

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T1 - Generating insights through data preparation, visualization, and analysis: Framework for combining clustering and data visualization techniques for low-cardinality sequential data

AU - Nestorov, Svetlozar

AU - Jukić, Boris

AU - Jukić, Nenad

AU - Sharma, Abhishek

AU - Rossi, Sippo

AU - Jukic, Nenad

PY - 2019/10/1

Y1 - 2019/10/1

UR - https://doi.org/10.1016/j.dss.2019.113119

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DO - 10.1016/j.dss.2019.113119

M3 - Article

VL - 125

JO - Decision Support Systems

JF - Decision Support Systems

ER -

Generating insights through data preparation, visualization, and analysis: Framework for combining clustering and data visualization techniques for low-cardinality sequential data

Disciplines

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