TY - JOUR
T1 - Survey of State-of-the-Art Mixed Data Clustering Algorithms
AU - Ahmad, Amir
AU - Khan, Shehroz S.
N1 - Funding Information:
This work was supported by the UAE University Start-Up Grant through Fund 31T101 under Grant G00002668.
Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field.
AB - Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field.
KW - Categorical features
KW - clustering
KW - mixed datasets
KW - numeric features
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U2 - 10.1109/ACCESS.2019.2903568
DO - 10.1109/ACCESS.2019.2903568
M3 - Article
AN - SCOPUS:85064644861
SN - 2169-3536
VL - 7
SP - 31883
EP - 31902
JO - IEEE Access
JF - IEEE Access
M1 - 8662561
ER -