Contents
What is constraint based clustering?
Constrained clustering is a semisupervised approach to clustering data while incorporating domain knowledge in the form of constraints. The constraints are usually expressed as pairwise statements indicating that two items must, or cannot, be placed into the same cluster.
Does KMeans work with categorical data?
The k-Means algorithm is not applicable to categorical data, as categorical variables are discrete and do not have any natural origin. So computing euclidean distance for such as space is not meaningful. k-Prototype is an extension of the k-Modes algorithm that works for mixed categorical and numerical features.
What is model based clustering?
This is a form of clustering which assumes that the data comes from a particular probability model. The model is based on 3 general assumptions: We know the number of clusters before we start. Each observation in the data as a certain probability of belonging to each cluster.
Why is K-means clustering sensitive to outliers?
The K-means clustering algorithm is sensitive to outliers, because a mean is easily influenced by extreme values. K-medoids clustering is a variant of K-means that is more robust to noises and outliers. The group of points in the right form a cluster, while the rightmost point is an outlier.
How does Kmeans handle categorical data?
The k-modes algorithm uses a simple matching dissimilarity measure. to deal with categorical objects, replaces the means of clusters with modes, and uses a frequency-based method to. update modes in the clustering process to minimize the clustering cost function.
Can decision trees be used for clustering?
Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. Decision trees are well-known tools to solve classification problems.
How are instance level constraints used in clustering?
• Historically, instance level constraints motivated by the availability of labeled data – i.e., Much unlabeled data and a little labeled data available generally as constraints, e.g., in web page clustering • This knowledge can be encapsulated using instance level constraints [Wagstaff et al. ’01] – Must-Link Constraints
How are constraints used in web page clustering?
– i.e., Much unlabeled data and a little labeled data available generally as constraints, e.g., in web page clustering • This knowledge can be encapsulated using instance level constraints [Wagstaff et al. ’01] – Must-Link Constraints • A pair of points siand sj(i ≠j) must be assigned to the same cluster.
How is Euclidean distance used in clustering with constraints?
Euclidean distance on Color Coherence Vectors returns both images as similar to query image Nearest Neighbor Image retrieval Given a query image return the K-nearest neighbors of the image from the database. 13 © Basu and Davidson 2005Clustering with Constraints 25
How to calculate the centroids of a cluster?
1. Randomly assign each instance to a cluster 2. Calculate the centroids for each cluster 3. For each instance • Calculate the distance to each cluster center • Assign the instance to the closest cluster 4. Goto 2 until distortion is small © Basu and Davidson 2005Clustering with Constraints 12 K Means Example (k=2)