A k-mean clustering algorithm for mixed numeric and categorical data
By Amir Ahmad, Lipika Dey. Data & Knowledge Engineering, 2007
747 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/1b688a2191c5ddd02d7b2bb828255a57d2f50ef4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Essam Soliman Debie
- Javed Asharf
- Abdul Wahab
- Hasnat Khurshid
- Waqas Haider
- Raihan Seraj
- Rui Fan
- Mingchun Wang
In Signal Processing
- Essam Soliman Debie
- Javed Asharf
- Abdul Wahab
- Hasnat Khurshid
- Waqas Haider
- Raihan Seraj
- Rui Fan
- Meilin Liu
Citations in the corpus
- A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, neutral): “improved [102].”
- Coupled nominal similarity in unsupervised learning (2011, methodology): “In fact, the dissimilarity that Ahmad and Dey [1] have used for mixed data clustering corresponds to the worst measure IRSP discussed here.”
- Inferring the outcomes of rejected loans: an application of semisupervised clustering (2019, methodology): “…-means method partitions groups by employing an iterative strategy to minimize the cost ζ , which is defined as the dissimilarities between objects (Ahmad and Dey, 2007): where d.X i , Q l / is the distance of the element X i from its closest cluster centre Q l , integer q determines the nature of…”
- A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, positive): “K-Means [157,158,164] DoS, Probe, R2L, U2R -k-Means clustering does not require labeled data.”
- The k-means Algorithm: A Comprehensive Survey and Performance Evaluation (2020, background): “[61] offered a cost function and distance measure for clustering datasets with mixed data (datasets with numerical and categorical data) based on co-occurrences of values.”
- Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding (2019, methodology): “Hence, we search for the threshold along the principal diagonal of the 2D histogram using kmean clustering [43].”