Dynamic Feature Selection for Clustering High Dimensional Data Streams
نویسندگان
چکیده
منابع مشابه
Clustering High Dimensional Dynamic Data Streams
We present data streaming algorithms for the kmedian problem in high-dimensional dynamic geometric data streams, i.e. streams allowing both insertions and deletions of points from a discrete Euclidean space {1, 2, . . .∆}. Our algorithms use k −2poly(d log ∆) space/time and maintain with high probability a small weighted set of points (a coreset) such that for every set of k centers the cost of...
متن کاملFeature Selection for Clustering on High Dimensional Data
This paper addresses the problem of feature selection for the high dimensional data clustering. This is a difficult problem because the ground truth class labels that can guide the selection are unavailable in clustering. Besides, the data may have a large number of features and the irrelevant ones can ruin the clustering. In this paper, we propose a novel feature weighting scheme for a kernel ...
متن کاملFeature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...
متن کاملFeature Selection For High-Dimensional Clustering
We present a nonparametric method for selecting informative features in high-dimensional clustering problems. We start with a screening step that uses a test for multimodality. Then we apply kernel density estimation and mode clustering to the selected features. The output of the method consists of a list of relevant features, and cluster assignments. We provide explicit bounds on the error rat...
متن کاملHigh Dimensional Data Clustering Using Fast Cluster Based Feature Selection
Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of fea...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2932308