Book Chapter10.1007/978-3-030-43449-6_20
Data Mining Algorithms for Knowledge Extraction
Stancu Ana-Maria Ramona,Cristescu Marian Pompiliu,Miglena Stoyanova +2 more
- 20 Sep 2019
- pp 349-357
17
TL;DR: From the studied algorithms, the clustering algorithms are emphasized, more precisely on the K-means algorithm, which was first studied using the Euclidean distance, then modified and studied the distance between the clusters.
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Abstract: In this paper, we study the methods, techniques, and algorithms used in data mining, and from the studied algorithms, we emphasized the clustering algorithms, more precisely on the K-means algorithm. This algorithm was first studied using the Euclidean distance, then modifying the distance between the clusters using the distances Mahalanobis and Canberra. After implementing the algorithms in C/C++, we compared the clustering of the three algorithms, after which we modified them and studied the distance between the clusters.
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The Impact of Data Science Solutions on the Company Turnover
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The Last Mile Issue and Urban Logistics: Choosing Parcel Machines in the Context of the Ecological Attitudes of the Y Generation Consumers Purchasing Online
TL;DR: In this article, the authors explored the relationship between environmental attitudes and behaviors of Generation Y and their propensity to make purchases over the internet and collect them using parcel machines and found that young people would be willing to pay a bit more for an item if it were a form of an environment saving measure.
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A Modified k-means Algorithm to Avoid Empty Clusters
Malay K. Pakhira
- 01 Jan 2009
TL;DR: It is shown that the proposed algorithm is semantically equivalent to the original k-means and there is no performance degradation due to incorporated modification, and results of simulation experiments using several data sets prove this claim.
81
A Modified k-means Algorithm to Avoid Empty
Clusters Malay,K. Pakhira +1 more
- 01 Jan 2009
TL;DR: In this article, a modified version of the k-means algorithm is presented, which is semantically equivalent to the original k -means and there is no performance degradation due to incorporated modification.
74
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