data clustering
IP2P K-means: an efficient method for data clustering on sensor networks
... sensor’s data clustering, it is possible to get an overall wisdom to the manner of data distribution and clustering is the first step for processing the data (Aioffi, Valle et ...2011). ...
6
Performance Evaluation of Affinity Propagation Approaches on Data Clustering
... from data is really ...from data, one of them is clustering. Clustering is commonly used to analyze data which is have very large or even huge data in numbers and the class label ...
10
Accelerated Simplified Swarm Optimization with Exploitation Search Scheme for Data Clustering.
... Empirical analysis of algorithm efficiency. The results from the previous subsection demonstrate that the proposed algorithm can perform better than its competitors in terms of the quality of solutions. Also, it can be ...
19
A Cluster Feature-Based Incremental Clustering Approach to Mixed Data
... as Clustering (Seokkyung and McLeod, 2005). Clustering is an important process for condensing and summarizing information because it is capable of providing a synopsis of the stored ...of clustering ...
6
EXPLORATORY GEOSPATIAL DATA ANALYSIS USING SELF-ORGANIZING MAPS
... perform data clustering and ...analysis, data analysis, voice recognition, process control and optimization (Kohonen, ...high-dimensional data space to a low-dimensional space, typically ...
158
Adaptação de viés indutivo de algoritmos de agrupamento de fluxos de dados
... on data clustering. In order to cope with the characteristics of data streams, rese- archers have designed clustering algorithms with low time and space complexity ...of clustering to ...
134
Clustering of Preprocessed Web Usage Data Using ART1 Neural Network and Comparative Analysis of ART1, K-means and SOM Clustering Techniques
... usage data clustering has been widely used for increasing Web information accessibility, understanding users’ navigation behavior, improving information retrieval and content delivery on the ...
9
A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets
... Abstract- Data clustering is an unsupervised method for extraction hidden pattern from huge data ...dimensional data sets with enormous number of samples is a challenging ...dimensional ...
5
Geographic Spatiotemporal Dynamic Model using Cellular Automata and Data Mining Techniques
... applying data mining techniques. Three data mining techniques were applied, namely finding sequential patterns in the data, clustering analysis of the pattern, and classification of a new ...
9
COMBINATION OF DIFFERENCE SUBSPACE AND OPPURTUNISTIC CLUSTERING ON HIGH DIMENSIONAL DATA
... analysing data and concentric effort has been taken in different domains comprises of recognition of pattern, statistical analysis and data mining for ...Subspace clustering is developed from the ...
8
Bagging Support Vector Machines for Leukemia Classification
... training data = {( , )} ∈ ℝ × {−1, +1} the aim of the classification is to find a function ( ( ) = ) that correctly classifies the patterns of the training data correctly, where is a n-dimensional vector ...
4
Handwriting and Speech Prototypes of Parkinson Patients: Belief Network Approach
... 4 Clustering result and feature characteristics of voice ...this clustering, and based on the above interpretation, we can say that patients in C1 compared to C2 have better physiological ...
7
Online clustering of trajectory data stream
... the Clustering approaches. Data stream applications im- pose a limited memory constrained, it becomes difficult to provide arbitrary-shaped clustering results using conventional algorithms as ...
115
EECHDA: Energy Efficient Clustering Hierarchy and Data Accumulation For Sensor Networks
... In the proposed model, the number of clusters, q, are pre- determined for the wireless sensor network. At the beginning, a set of CHs are chosen on random basis. The sensor nodes closer to the base station can directly ...
8
AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
... K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial ...
5
Decentralized Lifetime Maximizing Tree with Clustering for Data Delivery in Wireless Sensor Networks
... We have discussed before that our main focus has been on distance minimization between the nodes, minimization of energy utilization and efficient utilization of bandwidth. Considering the problem sensor network is ...
9
Mineração de Séries Temporais
... KEOGH E.; KASETTY S. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In Data Min. Knowl. Discov, volume 7, pages 349 – 371, 2003. KEOGH E. A Decade of Progress in ...
49
Kafkaesque power and bureaucracy
... We gathered data from 18 interviews. A first e-mail, Facebook, mobile phone or personal contact, explaining the request and context of the proposed interview, preceded data collection. Interviews were ...
44
Clustering search
... CS can be splitted off in four conceptually independent parts: a) the search metaheuristic (SM), b) the iterative clustering (IC) component, c) the analyzer module (AM), and d) the local searcher (LS). Figure 1 ...
17
MANAGEMENT ZONES USING FUZZY CLUSTERING BASED ON SPATIAL- TEMPORAL VARIABILITY OF SOIL AND CORN YIELD
... ABSTRACT: Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these ...c-means ...
14