Recently, some statistical studies have been done using the shape data. One of these studies is clustering shape data, which is the main topic of this paper. We are going to study some clustering algorithms on shape data and then introduce the best algorithm based on accuracy, speed, and scalability criteria. In addition, we propose a method for representing the shape data that facilitates and ...

Our approach to filtering involves a two-level dynamic clustering technique. Each filtering topic is used to create a primary cluster that forms a general profile for the topic. Documents that are attracted into a primary cluster participate in a topic-specific second level clustering process yielding what we refer to as secondary clusters. These secondary clusters, depending upon their status,...

In this paper we present a comprehensive approach to conceptual structuring and intelligent navigation of text databases. Given any collection of texts, we first automatically extract a set of index terms describing each text. Next, we use a particular lattice conceptual clustering method to build a network of clustered texts whose nodes are described using the

This paper presents a clustering algorithm for dot patterns in n-dimensional space. The n-dimensional space often represents a multivariate (nf -dimensional) function in a ns-dimensional space (ns + nf = n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in nf -dimensional space is performed to detect the sets of dots in n-dimensiona...

A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimension (N D > 3). The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering, however, new techniques are also introduced based on the path length between partitions that are connected to one an...

Capturing application semantics and allowing a human analyst to express his focus in mining have been the motivation for several recent studies on constrained mining. In this paper, we introduce and study the problem of constrained clustering| nding clusters that satisfy certain user-speci ed constraints. We argue that this problem arises naturally in practice. Two types of constraints are disc...

When multiple data sources are available for clustering, an a priori data integration process is usually required. This process may be costly and may not lead to good clusterings, since important information is likely to be discarded. In this paper we propose constrained clustering as a strategy for integrating data sources without losing any information. It basically consists of adding the com...

For the past decade, the need of multimedia mining has increased tremendously, especially in image data due to inexpensive digital technologies and fast mounting of image data. In this paper, we, first, show an algorithm, SpIBag (Spatial Item Bag Mining), which discovers frequent spatial patterns in images. Due to the properties of image data, SpIBag considers a bag of items together with a spa...

because of the huge amounts of proteomic data and demand for new methods of laboratory analysis results, proteins collective analysis, in addition to taking less time, biostatistician assist at identification of new patterns in the data set. in this study, rat hippocampus proteome in normal and alzheimer's disease (ad) were analyzed by using proteomic techniques and bioinformatics’ analysis. pr...

The hierarchical clustering methods based on vertex similarity have the advantage that global evaluation can be incorporated for community discovery. Vertex similarity metric is the most important part of these methods. However, the existing methods do not perform well for community discovery compared with the state-ofthe-art algorithms. In this paper, we propose a new vertex similarity metric ...