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efficiency of k means algorithm in data mining and other clustering algorithm

  • Cluster analysis - Wikipedia

    Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition ...

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  • K- Means Clustering Algorithm Applications in Data Mining ...

    Keywords: k-means,clustering, data mining, pattern recognition 1. Introduction treated collectively as one group and so may be considered The k-means algorithm is the most popular clustering tool used in scientific and industrial applications[1]. The k-means algorithm is best suited for data miningbecause of its

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  • CiteSeerX — A Fast Clustering Algorithm to Cluster Very ...

    CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means algorithm is best suited for implementing this operation because of its efficiency in clustering large data sets. However, working only on numeric values limits its use in data mining because data sets ...

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  • EFFICIENT K-MEANS CLUSTERING ALGORITHM USING …

    IV. K-MEANS CLUSTERING ALGORITHM K-means clustering is a well known partitioning method. In this objects are classified as belonging to one of K-groups. The results of Partitioning method is a set of K clusters, each object of data set belonging to one cluster. In each cluster there may be a centroid or a cluster representative.

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  • Efficient High Dimension Data Clustering using Constraint ...

    and k-means algorithm for efficient clustering of high dimensional data. First, by discarding the superfluous attributes by means of the reduct concept of rough set theory, it has identified the low dimensional space in the high dimensional data set. Then, it has identified suitable clusters by employing the k-means algorithm

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  • A differential privacy protecting K-means clustering ...

    This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved ...

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  • K-HARMONIC MEANS DATA CLUSTERING WITH …

    K-Harmonic Means data clustering with Imperialist Competitive Algorithm 95 Fig. 2. Flowchart of the Imperialist Competitive Algorithm 3. Related Works As mentioned before, the KHM algorithm has some drawbacks that one of the most important of them is the local optima problem. In the recent years, to No Yes No Yes Create the initial em pires

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  • Improvement and Parallelism of k-Means Clustering Algorithm

    The k-means clustering algorithm is one of the most commonly used algorithms for clustering analysis. The traditional k-means algorithm is, however, inefficient while working on large numbers of data sets and improving the algorithm efficiency remains a problem. This paper focuses on the efficiency issues of cluster algorithms.

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  • k-means clustering - Wikipedia

    k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.This results in a partitioning of the data space into Voronoi cells.

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  • K-Means Clustering in R Tutorial (article) - DataCamp

    K-means clustering is the most commonly used unsupervised machine learning algorithm for dividing a given dataset into k clusters. Here, k represents the number of clusters and must be provided by the user. You already know k in case of the Uber dataset, which is 5 or the number of boroughs. k-means is a good algorithm choice for the Uber 2014 ...

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  • Normalization based K means Clustering Algorithm

    comparative analysis of traditional K-means clustering algorithm with N-K means algorithm. Both the algorithms are run for different values of k. From the comparisons we can make out that N-K means algorithm outperforms the traditional K-means algorithm in terms …

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  • (PDF) A Clustering Method Based on K-Means Algorithm

    We collected the data from 38 rain gauges randomly located in a rectangular section of Idifu (Figure 2). The rain gauge positions were defined using the K-means clustering algorithm method [25 ...

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  • K Means Clustering Algorithm | Machine Learning Algorithm

    K Means Clustering Algorithm Uses. The K means clustering algorithm has a number of different uses. It can help in making sense of a massive amount of data that may have no obvious correlation. Data is useless if it is a simple group of numbers with no relation to each other. Categories help point at relationships and trends between numbers.

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  • An efficient k-means clustering algorithm: analysis and ...

    Abstract—In k-means clustering, we are given a set of ndata points in d-dimensional space Rdand an integer kand the problem is to determineaset of kpoints in Rd,calledcenters,so as to minimizethe meansquareddistancefromeach data pointto itsnearestcenter. A popular heuristic for k-means clustering is Lloyd’s algorithm.

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  • CiteSeerX — An efficient k-means clustering algorithm ...

    In this paper, we present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways.

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  • (PDF) Improving the Accuracy and Efficiency of the K-Means ...

    Proceedings of the World Congress on Engineering 2009 Vol I WCE 2009, July 1 - 3, 2009, London, U.K. Improving the Accuracy and Efficiency of the k-means Clustering Algorithm K. A. Abdul Nazeer, M. P. Sebastian Abstract— Emergence of modern techniques for scientific data for improving the accuracy and efficiency of the k-means collection has resulted in large scale accumulation of data per ...

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  • Big data-informed energy efficiency assessment of China ...

    May 10, 2018· K-means clustering is one of the most famous clustering methods, which is widely used in the field of data analysis due to its simplicity and high efficiency. K-means algorithm today has many variants like Fuzzy C-means clustering, K-medoids and Spherical means …

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  • The 5 Clustering Algorithms Data Scientists Need to Know

    Feb 05, 2018· The 5 Clustering Algorithms Data Scientists Need to Know. George Seif. Follow. ... On the other hand, K-Means has a couple of disadvantages. Firstly, you have to select how many groups/classes there are. This isn’t always trivial and ideally with a clustering algorithm we’d want it to figure those out for us because the point of it is to ...

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  • Efficient K-Means Clustering Algorithm in Web Log Mining

    Aiming at addressing the shortage of K-means algorithm, this paper presents an improved clustering algorithm by combining fuzzy matrix algorithm with K-means algorithm. The rest of the paper is organized as follows: Section 2 emphasizes the requirements of an improved data clustering algorithm with regard to Web log clustering.

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  • Clustering and Classifying Diabetic Data Sets Using K ...

    The most attractive property of the k-means algorithm in data mining is its efficiency in clustering large data sets. Classification is a data mining technique used to predict group membership for data instances. The classification is done using this algorithm and successfully classified the data set into two class labels namely tested_positive and

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  • efficiency of k means algorithm in data mining and other ...

    K Means Clustering Algorithm Appliions in Data Mining . Keywords: kmeans,clustering, data mining, pattern recognition 1. Introduction treated collectively as one group and so may be considered The kmeans algorithm is the most popular clustering tool used in scientific and industrial appliions[1].

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  • Data Mining Cluster Analysis: Basic Concepts and Algorithms

    Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 ... from (or unrelated to) the objects in other groups Inter-cluster distances are maximized Intra-cluster distances are minimized ... K-means Clustering OPartitional clustering approach …

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  • Efficiency of k-Means and K-Medoids Algorithms for ...

    Means algorithm can be run multiple times to reduce . 2.1. The k-Means Algorithm. The k-Means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori [10, 11].

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  • Data Mining - Clustering

    Simple Clustering: K-means Basic version works with numeric data only 1) Pick a number (K) of cluster centers - centroids (at random) 2) Assign every item to its nearest cluster center (e.g. using Euclidean distance) 3) Move each cluster center to the mean of its assigned items 4) Repeat steps 2,3 until convergence (change in cluster

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  • Proceedings of the World Congress on Engineering 2009 Vol ...

    for improving the accuracy and efficiency of the k-means algorithm. II. THE K-MEANS CLUSTERING ALGORITHM This section describes the original k-means clustering al-gorithm. The idea is to classify a given set of data into k number of disjoint clusters, where the value of k is fixed in advance. The algorithm consists of two separate phases: the

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  • Clustering Using K-means Algorithm

    This article explains K-means algorithm in an easy way. I’d like to start with an example to understand the objective of this powerful technique in machine learning before getting into the algorithm, which is quite simple. So imagine you have a set of numerical data of cancer tumors in 4 different ...

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  • Efficiency of K-Means Clustering Algorithm in Mining ...

    ABSTRACT - This paper presents the performance of k-means clustering algorithm, depending upon various mean values input methods. Clustering plays a vital role in data mining. Its main job is to group the similar data together based on the characteristic they possess. The mean values are the centroids of the specified number of cluster groups.

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  • (PDF) Analysis & Implementation of Clustering Data Mining ...

    Analysis & Implementation of Clustering Data Mining Technique -An Approach to Efficient K- means Algorithm. Ijaems Journal. Download with Google Download with Facebook or download with email. Analysis & Implementation of Clustering Data Mining Technique -An Approach to Efficient K- means Algorithm.

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  • Factors Affecting Efficiency of K-means Algorithm

    reducing the complexity of K-means algorithm. Keywords: Clustering, Data Mining, Initial Centroids, K-means. 1. INTRODUCTION. In the process of data mining, meaningful patterns are discovered from large datasets with an intention to support efficient decision making. Clustering is an important stepin all

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  • Case Study on Enhanced K-Means Algorithm for ...

    clustering, many research works carry outs on the k-means algorithm. A comparative analysis on k-means carried out by Kavya and Desai [13]. The k-means algorithm is compared with fuzzy c-mean algorithm, and it shows that the k-means algorithm outperforms said algorithm for the data …

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  • (PDF) Improving the Accuracy and Efficiency of the k-means ...

    Clustering is one of the main methods of data mining. K-means algorithm is one of the most common clustering algorithms due to its efficiency and ease of use. ... and the k-means clustering ...

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  • Data Mining Algorithms - 13 Algorithms Used in Data Mining ...

    Sep 17, 2018· The initial position of the centroids is thus very important. Since this position affects all the future steps in the K-means clustering algorithm. Hence, it is always advisable to keep the cluster centers as far away from each other as possible. If there are too many clusters, then clusters resemble each other.

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  • K-means Clustering Algorithm: Know How It Works | Edureka

    K-means Clustering Algorithm: Know How It Works; ... While implementing any algorithm, computational speed and efficiency becomes a very important parameter for end results. ... You can achieve this by k-means clustering and divide the entire data into different clusters and do further analysis based on the popularity.

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