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Clustering: An art of grouping related objects
... to clustering. Clustering is a subjective process where subjectivity describes different partitions of same data sets for different ...the clustering process ...based clustering algorithms ... See full document
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Ensemble Clustering Approaches Applied in Group-based Collaborative Filtering Supported by Multiple Users’ Feedback
... ensemble clustering techniques, in order to maintain the characteristics of the data in the clustering ...ensemble clustering techniques to combine the final results of each clustering ... See full document
17
A Novel Biomedical Knowledge Base for Genomic and Proteomic Analysis using Graph Clustering and Collaborative Filtering
... based clustering, depth first search and Bayesian rose tree representation would provide an efficient and easy solution for representing the gene terms and identifying the associated diseases for a particular gene ... See full document
11
English Sentence Recognition Based on HMM and Clustering
... the clustering cross-grouping algorithm and the HMM grouping algorithm, which are proposed for the implementation of the speaker-independent English sentence recognition system based on HMM and ... See full document
6
Grouping related attributes
... suggests clustering as a possible ...demonstrate, clustering does not provide the complete answer. A survey of clustering algorithms [26, 23, 28, 22, 41, 27] reveals basic patterns across ...of ... See full document
75
Outlier Detection Technique in Data Mining: A Research Perspective
... It assigns to each object a degree to be an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that, the degree depends on how isolated the object is with respect to the ... See full document
9
SEARCH ENGINE INDEXING USING K-MEAN CLUSTERING TECHNIQUE
... Hierarchical clustering [7] .This algorithm works by grouping the data one by one on the basis of the nearest distance measure of all the pairwise distance between the data ...continue grouping the ... See full document
10
Art and ephemera post-structuralist perspective: visual art, ephemera and environment
... the art industry that is not required to archive and much less to enter into commercial ...context, art and ephemera can upset the balance of archive as the dominant visual arts ... See full document
141
Performance Analysis of Channel Equalizers in Optical Communication for Next Generation Systems
... of objects into groups so that the objects in the same cluster are more similar to each other than to those in other ...clusters. Clustering is a key task of explorative data mining, and a common ... See full document
5
Clustering.ppt
... characteristics found in the data and grouping similar data objects into clusters... What is Cluster Analysis.[r] ... See full document
40
Clustering for networks of moving objects
... Similar approach is taken by other algorithms. For example, Konstantopou- los [36] present a solution where the moving node chooses the clusterhead that has the highest probability of still being the neighbour in the ... See full document
19
A Surveillance of Clustering Multi Represented Objects
... new clustering methods would require to restrict the analysis to a single representation or to construct a feature space comprising all ...data objects might not provide all possible ...many ... See full document
9
A case report: using SNOMED CT for grouping Adverse Drug Reactions Terms
... cept. The "one-to-one" or "one-to-few" relationship is very important in our model where the discriminative power is given by the SNOMED CT hierarchy (Figure 1). Ensuring consistent synonymy across ... See full document
8
ABSTRACT :Conventional clustering algorithms which are been used in the Data mining concept, using k-means
... R provides a wide variety of graphical and statistical techniques, which includes linear and nonlinear model, time-series analysis, classical statistical tests, clustering, classification, and other mining ... See full document
7
Online Full Text
... feature clustering is ...feature clustering is then introduced followed by different new fuzzification techniques in selection of features from clusters in section ... See full document
6
by Chance Enhancing Interaction with Large Data Sets Through Statistical Sampling
... 2.2.6 clustering and representative values Clustering techniques reduce the density of large datasets by grouping the data into a small number of groups of similar items.. These clusters[r] ... See full document
10
Data-driven analysis of ultrasonic pressure tube inspection data
... Defects on the inside surface of CANDU pressure tubes facilitate DHC which can lead to coolant leakage and can potentially damage the reactor. This work has introduced a new approach for identifying these defects through ... See full document
10
Clustering Analysis Using Hierarchy Grouping
... Obvious way to partition N points is splitting these points into N groups by having only one point in group. Then we can partition into N-1 groups, then into N-2 and so on. The process of partitioning is on the k-th ... See full document
5
Study of People Movement on the Throughput of Wi-Fi Networks and the Significant Changes that Affect WLAN Performance By Using Brownian Motion Mobility Model, Bounded Random Mobility Model & MobiSim v3 software To Model The J N T University Hyderabad India
... In our study, we examined the operation of these two models with two methods without any grouping and with grouping and clustering and we proved that the use of[r] ... See full document
27
Performance Analysis of Hybrid approach of Clustering Algorithms
... Data Clustering : These algorithms are specifically developed for data where Euclidean, or other numerical-oriented, distance measures cannot be ...based clustering oriented towards geographical data, and ... See full document
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