Subspace clustering for high dimensional datasets
نویسندگان
چکیده
منابع مشابه
A Mutual Subspace Clustering Algorithm for High Dimensional Datasets
Generation of consistent clusters is always an interesting research issue in the field of knowledge and data engineering. In real applications, different similarity measures and different clustering techniques may be adopted in different clustering spaces. In such a case, it is very difficult or even impossible to define an appropriate similarity measure and clustering criteria in the union spa...
متن کاملSubspace Clustering for High Dimensional Categorical Data
A fundamental operation in data mining is to partition a given dataset into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria [2]. These criteria are usually defined in the form of some distance, and similarity is hence defined as follows, the smaller the distance is, the more similar the objects a...
متن کاملSoft Subspace Clustering for High-Dimensional Data
High dimensional data is a phenomenon in real-world data mining applications. Text data is a typical example. In text mining, a text document is viewed as a vector of terms whose dimension is equal to the total number of unique terms in a data set, which is usually in thousands. High dimensional data occurs in business as well. In retails, for example, to effectively manage supplier relationshi...
متن کاملDBSC: A Dependency-Based Subspace Clustering Algorithm for High Dimensional Numerical Datasets
We present a novel algorithm called DBSC, which finds subspace clusters in numerical datasets based on the concept of “dependency”. This algorithm uses a depth-first search strategy to find out the maximal subspaces: a new dimension is added to current k-subspace and its validity as a (k 1)-subspace is evaluated. The clusters within those maximal subspaces are mined in a similar fashion as maxi...
متن کاملClustering for High Dimensional Data: Density based Subspace Clustering Algorithms
Finding clusters in high dimensional data is a challenging task as the high dimensional data comprises hundreds of attributes. Subspace clustering is an evolving methodology which, instead of finding clusters in the entire feature space, it aims at finding clusters in various overlapping or non-overlapping subspaces of the high dimensional dataset. Density based subspace clustering algorithms t...
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: International Journal of Advanced Computer Research
سال: 2016
ISSN: 2249-7277,2277-7970
DOI: 10.19101/ijacr.2016.625012