Major Project Report Submitted in Partial fulfillment of the
... variety of fields: psychology and other social sciences, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. ...
... variety of fields: psychology and other social sciences, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. ...
An Evolutionary Clustering Algorithm for Gene Expression
... set, these algorithms are not very practical when handling large data sets. An alternative data and cluster representation was proposed in [34], where the clustering problem is formulated as a graphpartitioning problem. Based on it, each data record is represented as a node in a graph and each node ...
... set, these algorithms are not very practical when handling large data sets. An alternative data and cluster representation was proposed in [34], where the clustering problem is formulated as a graphpartitioning problem. Based on it, each data record is represented as a node in a graph and each node ...
Efficient clustering techniques for managing large datasets
... group (= a cluster) consists of objects that are similar between themselves and dissimilar to objects of other groups. From the machine learning perspective, Clustering can be viewed as unsupervised learning of concepts [5]. A simple, formal, mathematical definition of clustering, as stated in [6] i ...
... group (= a cluster) consists of objects that are similar between themselves and dissimilar to objects of other groups. From the machine learning perspective, Clustering can be viewed as unsupervised learning of concepts [5]. A simple, formal, mathematical definition of clustering, as stated in [6] i ...
www.cs.laurentian.ca
... Summary of the statistics for a given subcluster: the 0-th, 1st, and 2nd moments of the subcluster from the statistical point of view ...
... Summary of the statistics for a given subcluster: the 0-th, 1st, and 2nd moments of the subcluster from the statistical point of view ...
Automatic Subspace Clustering of High Dimensional Data
... Our model can also be adapted to handle categorical data. An arbitrary order is introduced in the categorical domain. The partitioning scheme admits one categorical value in each interval and also places an empty interval between two different values. Consequently, if this dimension is chosen for cl ...
... Our model can also be adapted to handle categorical data. An arbitrary order is introduced in the categorical domain. The partitioning scheme admits one categorical value in each interval and also places an empty interval between two different values. Consequently, if this dimension is chosen for cl ...
Density Clustering Method for Gene Expression Data
... Computer Science Department North Dakota State University Fargo, ND 58105 Tel: (701) 231-6257 Fax: (701) 231-8255 {baoying.wang, william.perrizo}@ndsu.nodak.edu ...
... Computer Science Department North Dakota State University Fargo, ND 58105 Tel: (701) 231-6257 Fax: (701) 231-8255 {baoying.wang, william.perrizo}@ndsu.nodak.edu ...
Adaptive Grids for Clustering Massive Data Sets
... the steps of the adaptive grid technique. The domain of each dimension is divided into fine intervals, each of size x. The size of each bin, x, is selected such that each dimension has a minimum of 1000 fine bins. If the range of the dimension is from m to n then we set the number of bins in that dime ...
... the steps of the adaptive grid technique. The domain of each dimension is divided into fine intervals, each of size x. The size of each bin, x, is selected such that each dimension has a minimum of 1000 fine bins. If the range of the dimension is from m to n then we set the number of bins in that dime ...
Improving the Accuracy and Efficiency of the k-means
... each data-point and the initial centroids of all the clusters. The data-points are then assigned to the clusters having the closest centroids. This results in an initial grouping of the data-points. For each data-point, the cluster to which it is assigned (ClusterId) and its distance from the centro ...
... each data-point and the initial centroids of all the clusters. The data-points are then assigned to the clusters having the closest centroids. This results in an initial grouping of the data-points. For each data-point, the cluster to which it is assigned (ClusterId) and its distance from the centro ...
NPClu: A Methodology for Clustering Non
... Figure 1a illustrates a set of rectangles (rectangular shapes are popular in the spatial database literature; non-rectangular shapes can be approximated by their minimum bounding (hyper-) rectangles [4, 14]). The goal is to assign these rectangles to a number of clusters. The problem can be formally ...
... Figure 1a illustrates a set of rectangles (rectangular shapes are popular in the spatial database literature; non-rectangular shapes can be approximated by their minimum bounding (hyper-) rectangles [4, 14]). The goal is to assign these rectangles to a number of clusters. The problem can be formally ...
PPT
... – In fuzzy clustering, a point belongs to every cluster with some weight between 0 and 1 – Weights must sum to 1 – Probabilistic clustering has similar characteristics ...
... – In fuzzy clustering, a point belongs to every cluster with some weight between 0 and 1 – Weights must sum to 1 – Probabilistic clustering has similar characteristics ...
Improving K-Means by Outlier Removal
... the problem of nonoverlapping clusters. However, K-means remains probably the most widely used clustering method, because it is simple to implement and provides reasonably good results in most cases. In this paper, we improve the K-means based density estimation by embedding a simple outlier removal ...
... the problem of nonoverlapping clusters. However, K-means remains probably the most widely used clustering method, because it is simple to implement and provides reasonably good results in most cases. In this paper, we improve the K-means based density estimation by embedding a simple outlier removal ...
Subspace Clustering of High-Dimensional Data: An Evolutionary
... ORCLUS finds projected clusters as a set of data points C together with a set of orthogonal vectors such that these data points are closely clustered in the defined subspace. A limitation of these two approaches is that the process of forming the locality is based on the full dimensionality of the s ...
... ORCLUS finds projected clusters as a set of data points C together with a set of orthogonal vectors such that these data points are closely clustered in the defined subspace. A limitation of these two approaches is that the process of forming the locality is based on the full dimensionality of the s ...
Human genetic clustering
Human genetic clustering analysis uses mathematical cluster analysis of the degree of similarity of genetic data between individuals and groups in order to infer population structures and assign individuals to groups. These groupings in turn often, but not always, correspond with the individuals' self-identified geographical ancestry. A similar analysis can be done using principal components analysis, which in earlier research was a popular method. Many studies in the past few years have continued using principal components analysis.