منابع مشابه
Interactive Clustering
We consider the problem of clustering with feedback. We study a recently proposed framework for the problem and present new results on clustering geometric concept classes in that model. In this model the clustering algorithm interacts with the user via “split” and “merge” requests to figure out the target clustering. We also give a simple generic algorithm to cluster any concept class in the m...
متن کاملInteractive Bayesian Hierarchical Clustering
Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user’s needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering...
متن کاملClustering with Interactive Feedback
In this paper, we initiate a theoretical study of the problem of clustering data under interactive feedback. We introduce a query-based model in which users can provide feedback to a clustering algorithm in a natural way via split and merge requests. We then analyze the “clusterability” of different concept classes in this framework — the ability to cluster correctly with a bounded number of re...
متن کاملInteractive Unsupervised Clustering with Clustervision
Figure 1: An overview of Clustervision on a dataset describing 403 paintings by the “Joy of Painting” artist Bob Ross. (A) Ranked List of Clustering Results shows 16 different clustering results that are sorted by the aggregated quality measures; (B) Projection shows a selected clustering result (highlighted in yellow in (A)) on a projection of data points colored corresponding to corresponding...
متن کاملLocal algorithms for interactive clustering
We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show tha...
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ژورنال
عنوان ژورنال: Behavior Research Methods & Instrumentation
سال: 1982
ISSN: 1554-351X,1554-3528
DOI: 10.3758/bf03202148