Active Learning Entropy Sampling based Clustering Optimization Method for Electricity Data

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Active Learning Method for Constraint-Based Clustering Algorithms

Clustering Based Active Learning for Evolving Data Streams

Data labeling is an expensive and time-consuming task. Choosing which labels to use is increasingly becoming important. In the active learning setting, a classifier is trained by asking for labels for only a small fraction of all instances. While many works exist that deal with this issue in non-streaming scenarios, few works exist in the data stream setting. In this paper we propose a new acti...

The Learning-Curve Sampling Method Applied to Model-Based Clustering

We examine the learning-curve sampling method, an approach for applying machinelearning algorithms to large data sets. The approach is based on the observation that the computational cost of learning a model increases as a function of the sample size of the training data, whereas the accuracy of a model has diminishing improvements as a function of sample size. Thus, the learning-curve sampling...

Clustering-Based Active Learning

In contexts where obtaining labels for data points is expensive, active learning is a widely used methodology for deciding which data points to get a label for. In this project, we propose and evaluate an active learning algorithm in context of an automatic grading and feedback generation tool for an online embedded systems course currently under development at UC Berkeley. This tool learns a f...