{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:49:00Z","timestamp":1761648540412},"reference-count":35,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2018,6]]},"abstract":"<jats:p> Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are not linearly separable. Kernel k-Means is a state of the art clustering algorithm, which employs the kernel trick, in order to perform clustering on a higher dimensionality space, thus overcoming the limitations of classic k-Means regarding the non-linear separability of the input data. With respect to the challenges of Big Data research, a field that has established itself in the last few years and involves performing tasks on extremely large amounts of data, several adaptations of the Kernel k-Means have been proposed, each of which has different requirements in processing power and running time, while also incurring different trade-offs in performance. In this paper, we present several issues and techniques involving the usage of Kernel k-Means for Big Data clustering and how the combination of each component in a clustering framework fares in terms of resources, time and performance. We use experimental results, in order to evaluate several combinations and provide a recommendation on how to approach a Big Data clustering problem. <\/jats:p>","DOI":"10.1142\/s0218213018600060","type":"journal-article","created":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T08:28:59Z","timestamp":1524472139000},"page":"1860006","source":"Crossref","is-referenced-by-count":1,"title":["Big Data Clustering with Kernel <i>k<\/i>-Means: Resources, Time and Performance"],"prefix":"10.1142","volume":"27","author":[{"given":"Nikolaos","family":"Tsapanos","sequence":"first","affiliation":[{"name":"Department of Informatics, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Box 54 124, Greece"}]},{"given":"Anastasios","family":"Tefas","sequence":"additional","affiliation":[{"name":"Department of Informatics, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Box 54 124, Greece"}]},{"given":"Nikolaos","family":"Nikolaidis","sequence":"additional","affiliation":[{"name":"Department of Informatics, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Box 54 124, Greece"}]},{"given":"Ioannis","family":"Pitas","sequence":"additional","affiliation":[{"name":"Department of Informatics, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Box 54 124, Greece"}]}],"member":"219","published-online":{"date-parts":[[2018,6,25]]},"reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"p_3","first-page":"888","volume":"22","author":"Shi J.","year":"1997","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.255"},{"key":"p_5","first-page":"245","author":"Bingham E.","year":"2001","journal-title":"CA, USA"},{"key":"p_6","first-page":"873","author":"Church K. 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