Constrained K-means Clustering with Background Knowledge
نویسندگان
چکیده
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be profitably modified to make use of this information. In experiments with artificial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance.
منابع مشابه
Clustering Using Boosted Constrained k-Means Algorithm
This article proposes a constrained clustering algorithmwith competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learn...
متن کاملTwo Phase Semi-supervised Clustering Using Background Knowledge
Using background knowledge in clustering, called semi-clustering, is one of the actively researched areas in data mining. In this paper, we illustrate how to use background knowledge related to a domain more efficiently. For a given data, the number of classes is investigated by using the must-link constraints before clustering and these must-link data are assigned to the corresponding classes....
متن کاملAn Improved K-Means with Artificial Bee Colony Algorithm for Clustering Crimes
Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field o...
متن کاملClustering of nasopharyngeal carcinoma intensity modulated radiation therapy plans based on k-means algorithm and geometrical features
Background: The design of intensity modulated radiation therapy (IMRT) plans is difficult and time-consuming. The retrieval of similar IMRT plans from the IMRT plan dataset can effectively improve the quality and efficiency of IMRT plans and automate the design of IMRT planning. However, the large IMRT plans datasets will bring inefficient retrieval result. Materials and Methods: An intensity-m...
متن کاملAvoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links
In this paper we present a new clustering algorithm which extends the traditional batch k-means enabling the introduction of domain knowledge in the form of Must, Cannot, May and May-Not rules between the data points. Besides, we have applied the presented method to the task of avoiding bias in clustering. Evaluation carried out in standard collections showed considerable improvements in effect...
متن کامل