### A Fast Clustering Algorithm to Cluster Very Large ...

used to cluster categorical data. The algorithm, called k-modes, is an extension to the well known k-means algorithm (MacQueen 1967). Compared to other clustering methods the k-means algorithm and its variants (Anderberg 1973) are efficient in clustering large data sets, thus very suitable for data mining. However, their use is

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

EFFICIENT K-MEANS CLUSTERING ALGORITHM USING RANKING METHOD, K-means is a data mining algorithm which performs, other data set, . Inquiry; An Efficiency K-Means Data Clustering in Cotton Textile,, Simić S (2016) An Efficiency K-Means Data Clustering in, data mining clustering, k-means clustering algorithm with the . Inquiry

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### Introduction to K-means Clustering - Data science

Choosing K. The algorithm described above finds the clusters and data set labels for a particular pre-chosen K. To find the number of clusters in the data, the user needs to run the K-means clustering algorithm for a range of K values and compare the results.

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### Automatic K- Expectation Maximization (A K-EM) Algorithm ...

Automatic . K- Expectation Maximization (A K-EM) Algorithm for Data Mining Applications Archit Harsh. 1 and John E. Ball2. Abstract . A non-parametric data clustering technique for achieving efficient data-clustering and improving the number of clusters is presented in this paper. K-Means and Expectation-Maximization algorithms have been widely ...

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### 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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### When K-Means Clustering Fails: Alternatives for Segmenting ...

K-means is, after all, fairly easy to understand under the hood and very efficient with large data sets you might see in a big data solution environment. But like all statistical methods, K-means clustering has some underlying assumptions. Outside the "Sphere" of Influence . Real-life data …

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### Normalization based K means Clustering Algorithm - arXiv

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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### Data Mining for Marketing – Simple K-Means Clustering ...

· Process and algorithm The process. Data mining is the process of extracting, transforming, and analyzing the data in a set of data regardless of its size. For this case study, the data mining process was used to gather info regarding a fictional bank's clients.

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### A survey on Efficient Enhanced K-Means Clustering Algorithm

A survey on Efficient Enhanced K-Means Clustering Algorithm Uploaded by International Journal for Scientific Research and Development - IJSRD Data mining is the process of using technology to identify patterns and prospects from large amount of information.

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### Clustering Using K-means Algorithm

K-means is a fast and efficient method, because the complexity of one iteration is k*n*d where k (number of clusters), n (number of examples), and d (time …

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### An analysis of MapReduce efficiency in document clustering ...

The most popular clustering algorithm is k-means because of its simplicity and efficiency . ICDM Conference ranked it second of top 10 clustering algorithms . K-means algorithm groups N objects into K clusters maintaining high intra group similarity and low inter group similarity of the objects.

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### (PDF) An Efficient K-Means Clustering Algorithm

K-means clustering is one of the most widely used techniques in data mining [2, 11,15,16,20]. The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the ...

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### Data Mining for Marketing — Simple K-Means Clustering ...

Data mining is the process through which valid and previously unknown information is extracted from a specific set of data and is then used to make an important business decision.

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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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### A New and Efficient K-Means Clustering Algorithm

In this K-means clustering algorithm if the number of clusters is more the complexity and number of iterations are more. 3. PROPOSED K-MEANS CLUSTERING ALGORITHM In this paper we are going to propose A New and Efficient K-means clustering algorithm. K-means is a clustering

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### The New Era Of Clustering: CLOPE Algorithm in C#.NET ...

Also, unlike k-means clustering, the CLOPE algorithm belongs to the entire class of data mining machine learning algorithms, that exactly conform to "similar tastes …

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### Clustering Algorithms: K-Means, EMC and Affinity ...

After the necessary introduction, Data Mining courses always continue with K-Means; an effective, widely used, all-around clustering algorithm. Before actually running it, we have to define a distance function between data points (for example, Euclidean distance if we want to cluster points in space), and we have to set the number of clusters ...

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### A survey on Efficient Enhanced K-Means Clustering Algorithm

2) An efficient enhanced k-means clustering algorithm Fahim et al. [4] proposed k-means algorithm determines spherical shaped cluster, whose center is the magnitude center of points in that cluster, this center moves as new points are added to or detached from it.

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### An efficient enhanced k-means clustering algorithm ...

In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration.

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### Efficient K-Means Clustering Algorithm Using Ranking ...

K-Means clustering is a clustering method in which the given data set is divided into K number of clusters. This paper is intended to give the introduction about K-means clustering and its algorithm.

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### Data Mining With K-Means Clustering

The k-means algorithm is one of the simplest clustering techniques and it is commonly used in medical imaging, biometrics, and related fields. The advantage of k-means clustering is that it tells about your data (using its unsupervised form) rather than you having to instruct the algorithm …

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### An efficient k-means clustering algorithm

mance of the k-means algorithms in Section 2. We present our algorithm in Section 3. We describe the experimental results in Section 4 and we conclude with Section 5. 2 k-means Clustering In this section, we brieﬂy describe the direct k-means algorithm [9, 8, 3]. The number of clusters is assumed to be ﬁxed in k-means clustering. Let the ...

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