The phrase "automatic clustering" is correct and usable in written English.
It can be used in contexts related to data analysis, machine learning, or computer science, where it refers to the process of grouping data points automatically based on certain criteria.
Example: "The software utilizes automatic clustering to categorize the data without manual intervention."
Alternatives: "automated grouping" or "self-organizing clustering".
It can be seen from Fig. 11, the diagnostic sample (marked by "☆") is classified into the fault type of "liquid shortage in the pump" automatically by the proposed automatic clustering algorithm.
Based on clonal selection mechanism in immune system, a dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partition of datasets.
We thus automatically fitted the data with a single cluster when the automatic clustering resulted in cluster means that were less than 0.5 units (in log space) apart.
Automatic clustering allows abstraction of the DSMC algorithm from the physical system of the problem in question.
The proposed algorithm has been extensively compared with five state-of-the-art automatic clustering techniques over a suit of datasets.
Experiment results show that the proposed algorithm is superior or competitive to three state-and-the-art automatic clustering algorithms recently reported in the literature.
The result is a semi-automated mapping technique that accommodates the automatic clustering of the source model with the user's hypothesized knowledge about the system's architecture.
In addition to providing completely inductive automatic clustering, TextSmart also gives its users an option to supply categories of words that are to be treated as synonyms in the clustering process.
Automatic clustering is employed to group the multidimensional data into classes, and from each class training and testing data sets are selected for developing a back-propagation neural network to predict Microtox.
Inspired from the structure and inherent mechanism of membrane computing, a membrane clustering algorithm is proposed to deal with automatic clustering problem, in which a tissue-like membrane system with fully connected structure is designed as its computing framework.
One-step gradient descent genetic algorithm (OSGD-GA) is designed for optimizing the codebooks of the low-frequency wavelet coefficient by defining the importance degree of each coefficient and utilizing fuzzy membership to address the automatic clustering.
