Learning States and Rules for Detecting Anomalies in Time Series
Applied Intelligence
SMArTIC: towards building an accurate, robust and scalable specification miner
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Decision support in construction equipment management using a nonparametric outlier mining algorithm
Expert Systems with Applications: An International Journal
A nonparametric outlier detection for effectively discovering top-n outliers from engineering data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A new approach for cluster detection for large datasets with high dimensionality
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Enhancing density-based clustering: Parameter reduction and outlier detection
Information Systems
| Hi-index | 0.00 |
Clustering is the problem of grouping data based on similarityand consists of maximizing the intra-group similaritywhile minimizing the inter-group similarity. The problem ofclustering data sets is also known as unsupervised classification,since no class labels are given. However, all exist-ingclustering algorithms require some parameters to steerthe clustering process, such as the famous k for the numberof expected clusters, which constitutes a supervision ofa sort. We present in this paper a new, efficient, fast andscalable clustering algorithm that clusters over a range ofresolutions and finds a potential optimum clustering withoutrequiring any parameter input. Our experiments showthat our algorithm outperforms most existing clustering algorithmsin quality and speed for large data sets.