k-means PDF: 1 to 10 of 1431 results fetched - page 1 [is]

Zoolz is the only cloud solution that keeps your data even when you disconnect your drives

An Iterative Improved k-means Clustering

issuu.com/ideseditor/docs/aceee_ijns_2_3_183...
Clustering is a data mining (machine learning), unsupervised learning technique used to place data elements into related groups without advance knowledge of the group definitions. One of the most popular and widely studied clustering methods that minimize the clustering error for points in Euclidean space is called K-means clustering. However, the k-means method converges to one of many local minima, and it is known that the final results depend on the initial starting points (means). In this research paper, we have introduced and tested an improved algorithm to start the kmeans with good starting points (means). The good initial starting points allow k-means to converge to a better local minimum; also the numbers of iteration over the full dataset are being decreased. Experimental results show that initial starting points lead to good solution reducing the number of iterations to form a cluster.
Uploaded by ideseditor on 01/16/2013
Digital publication details: 4 pages.

Modelling Data Dispersion Degree in Automatic Robust Estimation for Mixture Models

issuu.com/sep2011--now/docs/aiaa10015...
http://www.aiaa-journal.org The trimming scheme with a prefixed cutoff portion is known as a method of improving the robustness of statistical models such as multivariate Gaussian mixture models (MG-MMs) in small scale tests by alleviating the impacts of outliers. However, when this method is applied to real-world data, such as noisy speech processing, it is hard to know the optimal cut-off portion to remove the outliers and sometimes removes useful data samples as well.
Uploaded by sep2011--now on 02/19/2013
Digital publication details: 13 pages.

Visual and analytical mining of transactions data for production planning for...

issuu.com/gurdalertek/docs/ertek_et_al_ims2004...
Recent developments in information technology pavedthe way for the collection of large amounts of data pertaining to various aspects of anenterprise. The greatest challenge faced in processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The primary phases of the proposed framework consist offiltering, clustering and comparison steps implemented using interactive pie charts, K-Means algorithm and parallel coordinate plots respectively. A prototype decision support system is developed and a sample analysis session is conducted to demonstrate the applicability of the framework.
Uploaded by gurdalertek on 12/09/2004
Digital publication details: 13 pages.

Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network

issuu.com/14110/docs/vol3-issue-1-02-prediction-of-at...
Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP) to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster.
Uploaded by 14110 on 01/10/2013
Digital publication details: 8 pages.

A PSO-Based Subtractive Data Clustering Algorithm

issuu.com/14110/docs/vol3-issue-2-01-a_pso-based_subt...
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods.
Uploaded by 14110 on 03/06/2013
Digital publication details: 9 pages.

Extraction of Circle of Willis from 2D MagneticResonance Angiograms

issuu.com/ideseditor/docs/aceee_ijit_2_1_547...
Magnetic resonance angiogram is a way to study cerebrovascular structures. It helps to obtain information regarding blood flow in a non-invasive fashion. Magnetic resonance angiograms are examined basically for detection of vascular pathologies, neurosurgery planning, and vascular landmark detection. In certain cases it becomes complicated for the doctors to assess the cerebral vessels or Circle of Willis from the two-dimensional (2D) brain magnetic resonance angiograms. In this paper an attempt has been made to extract the Circle of Willis from 2D magnetic resonance angiograms, so as to overcome such difficulties. The proposed method preprocesses the magnetic resonance angiograms and subsequently extracts the Circle of Willis. The extraction has been done by color-based segmentation using K-means clustering algorithm. As the developed method successfully extracts the vasculature from the brain magnetic resonance angiograms,
Uploaded by ideseditor on 01/10/2013
Digital publication details: 4 pages.

ELIZA FRASER - How Fraser Island Got it's Name

issuu.com/ge_h2/docs/eliza_fraser_-_how_fraser_island...
Once upon a time in the beautiful isle of Fraser Island, the indigenous inhabitants of the said island which are commonly called the Butchulla people gave Fraser Island the name of K'gari which means paradise. True to the meaning of the historical name of this isle , it is really a paradise which is richly blessed with abundant flora and fauna wildlife.
Uploaded by ge_h2 on 06/05/2012
Digital publication details: 1 pages.

Developing the Means for the Use of Modern Lighting

issuu.com/christinadianparmionova/docs/developing_the...
Apte, J. M. Fuller, A. Gopal, and K. Lindgren. 2007. "Developing the Means for the Use of Modern Lighting: How can WLED Technology Bring High Quality, Affordable Light to India's Poor?"
Uploaded by christinadianparmionova on 01/09/2012
Digital publication details: 43 pages.

Segmentation and Dimension Reduction: Exploratory and Model-Based Approaches

issuu.com/erim/docs/eps2009165mkt9058922014rosmalen...
In this thesis, we incorporate the modeling principles of segmentation and dimension reduction into statistical models. We thus develop new models that can summarize and explain the information in a data set in a simple way. The focus is on dimension reduction using bilinear parameter structures and techniques for clustering both modes of a two-mode data matrix. To illustrate the usefulness of the techniques, the thesis includes a variety of empirical applications in marketing, psychometrics, and political science. An important application is modeling the response behavior in surveys with rating scales, which provides novel insight into what kinds of response styles exist, and how substantive opinions vary among respondents. We find that our modeling approaches yield new techniques for data analysis that can be useful in a variety of applied fields.
Uploaded by erim on 01/24/2010
Digital publication details: 170 pages.

Design and Implementation of Data Mining Tools

issuu.com/iliastanov/docs/1_design_and_implementation...
Design and Implementation of Data Mining Tools
Uploaded by iliastanov on 03/20/2011
Digital publication details: 276 pages.
[1] 2345Next