Clustering Methods

Rokach, Lior; Maimon, Oded

doi:10.1007/0-387-25465-x_15

Cited by 946 publications

(496 citation statements)

References 23 publications

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“…Since the point of clustering is to discover another arrangement of classifications, the most recent gatherings are of enthusiasm for themselves, and their appraisal is inborn. [4] There is no earlier learning about information. The distinctive grouping techniques are Hierarchical Methods(HM), Partitioning Methods (PM), Density-based Methods(DBM), Model-based Clustering Methods(MBCM)…”

Section: Clusteringmentioning

confidence: 99%

A review of different techniques utilized for-casting crop yield

Bhardwaj¹,

Singh²

2018

IJET

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The farming structures the establishment of Indian economy. The harvest creation mainly depends on atmospheric conditions such as climate change, rain, soil etc., that impacts on yield improvement. The most of existing algorithms for crop yield prediction utilizes the existing data mining (DM) techniques for forecasting. This paper exhibits an overview on some of the existing techniques mostly used for crop yield prediction.

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Section: Clusteringmentioning

confidence: 99%

A review of different techniques utilized for-casting crop yield

Bhardwaj¹,

Singh²

2018

IJET

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“…The computational cost can get very high if a large number of objects and obstacles are involved. Representation by k-medoids has two advantages [9]. First, it presents no limitations on attributes types, and, second, the choice of medoids is dictated by the location of a predominant fraction of points inside a cluster and therefore, it is lesser sensitive to the presence of outliers.…”

Section: Partitioning Clusteringmentioning

confidence: 99%

Enhancing the Performance of K-Means Clustering by using Fuzzy Partitioning Matrix

Shaikh¹,

Patil²

2017

IJCA

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Clustering hast two approaches, Hard clustering and soft clustering. The hard clustering restricts that the data object in the given data belongs to exactly one cluster. The problem with hard K-Means (KM) clustering is that the different initial partitions can result in different final clusters. Soft clustering which also known as fuzzy clustering forms clusters such that data object can belong to more than one cluster based on their membership levels. But sometimes the resulting membership values do not always correspond well to the degrees of belonging of the data. So to overcome the problems in hard Fuzzy K-Means clustering, the improved Fuzzy K-Means (FKM) clustering approach is proposed. The proposed improved Fuzzy K-Means clustering assigns membership to an object inversely related to the relative distance of the object to cluster prototype. Fuzzy K-Means clustering assigns membership levels which indicate the degree to which the data elements belong to the clusters, and then using them to assign data object to one or more clusters. These indicate the strength of the association between that data object and a particular cluster. The proposed work also compares the execution time and required memory of Proposed Fuzzy KMeans (FKM) to that of existing Fuzzy K-Means clustering.

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“…For example a criteria might be the one of measuring the compactness of the clusters looking at the intracluster homogeneity, the inter-cluster separability or a combination of these two. Indices of this kind are, for example, Sum of Squared Error (SSE), Minimum Variance Criteria and Scatter Criteria [29].…”

Section: Clusteringmentioning

confidence: 99%

“…This method constructs the clusters by recursively partitioning the instances in either a top-down or bottom-up fashion [29]. Can be one of the following:…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

SNNAP: Solver-Based Nearest Neighbor for Algorithm Portfolios

Collautti

Malitsky

Mehta

et al. 2013

Advanced Information Systems Engineering

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A Mamma e Papà e ai miei fratelli AbstractThe success of portfolio algorithms in competitions in the area of combinatorial problem solving, as well as in practice, has motivated interest in the development of new approaches to determine the best solver for the problem at hand. Yet, although there are a number of ways in which this decision can be made, it always relies on a rich set of features to identify and distinguish the structure of the problem instances.In this thesis, however, it is firstly shown how not all the features in the problem have the same relevancy. Then it is presented how one of the most successful portfolio approaches, ISAC, can be augmented by taking into account the past performance of solvers as part of the feature vector. Testing on a variety of SAT datasets, it is here proved how the new formulation continuously outperforms an unmodified/standard version of ISAC.This thesis presents a novel strategy that tackles the problem of algorithm portfolios applying machine learning techniques and improves a state-of-the-art portfolio algorithms approaches (ISAC).Starting from the basic assumptions behind the standard ISAC methodology that the given collection of features can be used to correctly identify the structure of each instance, an important step has been to be able to improve clustering, grouping together instances that prefer to be solved by the same solver. It has been created, and here is presented, a methodology to redefine the feature vector including past performance of the solvers. The final result is named SNNAP: Solver-based Nearest Neighbor for Algorithm Portfolios which is here presented.

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Clustering Methods

Cited by 946 publications

References 23 publications

A review of different techniques utilized for-casting crop yield

A review of different techniques utilized for-casting crop yield

Enhancing the Performance of K-Means Clustering by using Fuzzy Partitioning Matrix

SNNAP: Solver-Based Nearest Neighbor for Algorithm Portfolios

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