HARP: a practical projected clustering algorithm
نویسندگان
چکیده
منابع مشابه
Efficient Algorithm for Projected Clustering
With high dimensional data, natural clusters are expected to exist in different subspaces. We propose the EPC (Efficient Projected Clustering) algorithm to discover the sets of correlated dimensions and the location of the clusters. This algorithm is quite different from previous approaches and has the following advantages: (1) no requirement of the input of the number of natural clusters, and ...
متن کاملPTS: Projected Topological Stream clustering algorithm
High-dimensional data streams clustering is an attractive research topic, as there are several applications that generate a high number of attributes, bringing new challenges in terms of partitioning due to the curse of dimensionality. In addition, those applications produce unbounded sequences of data which cannot be stored for later analysis. Although the importance of this scenario, there ar...
متن کاملType-2 Projected Gustafson-Kessel Clustering Algorithm
We propose a type-2 based clustering algorithm to capture data points and attributes relationship embedded in fuzzy subspaces. It is a modification of Gustafson Kessel clustering algorithm through deployment of type-2 fuzzy sets for high dimensional data. The experimental results have shown that type-2 projected GK algorithm perform considerably better than the comparative techniques. General T...
متن کاملA highly-usable projected clustering algorithm for gene expression profiles
Projected clustering has become a hot research topic due to its ability to cluster high-dimensional data. However, most existing projected clustering algorithms depend on some critical user parameters in determining the relevant attributes of each cluster. In case wrong parameter values are used, the clustering performance will be seriously degraded. Unfortunately, correct parameter values are ...
متن کاملSemi-Supervised Projected Clustering
Recent studies suggest that projected clusters with extremely low dimensionality exist in many real datasets. A number of projected clustering algorithms have been proposed in the past several years, but few can identify clusters with dimensionality lower than 10% of the total number of dimensions, which are commonly found in some real datasets such as gene expression profiles. In this paper we...
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 Transactions on Knowledge and Data Engineering
سال: 2004
ISSN: 1041-4347
DOI: 10.1109/tkde.2004.74