Mining Navigation Patterns in a Virtual Campus

Xhafa, Fatos; Martínez, Antonio López; Caballé, Santi; Koliçi, Vladi; Barolli, Leonard

doi:10.1109/eidwt.2012.65

Cited by 7 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, we would like to consider other data sets from other application domains such as Virtual Campus or autonomic computing systems [4], [9], [12].…”

Section: Discussionmentioning

confidence: 99%

Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluation

Xhafa

Bogza²,

Caballé

et al. 2016

2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)

Self Cite

View full text Add to dashboard Cite

“…Likewise, we would like to consider other data sets from other application domains such as Virtual Campus or autonomic computing systems [4], [9], [12].…”

Section: Discussionmentioning

confidence: 99%

Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluation

Xhafa

Bogza²,

Caballé

et al. 2016

2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)

Self Cite

View full text Add to dashboard Cite

“…Since its introduction, frequent pattern mining has been the subject of numerous studies, in which it has also played an important role in the mining of other patterns [6], [7], [25], [26], [28], [30]. Many of the early frequent pattern mining algorithms were Apriori-based [1], which depend on a generate-and-test paradigm to mine frequent patterns (or frequent itemsets) from transaction databases of precise data by first generating candidates and then checking their actual support (i.e., occurrences) against the database.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints

Leung

MacKinnon

Jiang

2014

2014 28th International Conference on Advanced Information Networking and Applications Workshops

View full text Add to dashboard Cite

High volumes of uncertain data can be generated in distributed environments in many real-life biological, medical, and life science applications. As an important data mining task, frequent pattern mining helps discover frequently co-occurring items, objects, or events from these distributed databases. However, users may be interested in only some small portions of all the frequent patterns that can be mined from these databases. In this paper, we propose an intelligent computing system that (i) allows users to express their interests via the use of userspecified constraints and (ii) effectively exploits anti-monotonic properties of user-specified constraints and efficiently discovers frequent patterns satisfying these constraints from the distributed databases containing uncertain data.

show abstract

“…The problem that arises is that there is a lot of log data generated and it is difficult to extract relevant information from it. Hadoop can be used for large log data processing [8], [24], [25].…”

Section: B Application Examples 1) Log Data Processingmentioning

confidence: 99%

Performance Evaluation of Mahout Clustering Algorithms Using a Twitter Streaming Dataset

Xhafa

Bogza

Caballé

2017

2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA)

Self Cite

View full text Add to dashboard Cite

'ús personal d'aquest material. S'ha de demanar permís a l'IEEE per a qualsevol altre ús, incloent la reimpressió/reedició amb fins publicitaris o promocionals, la creació de noves obres col·lectives per a la revenda o redistribució en servidors o llistes o la reutilització de parts d'aquest treball amb drets d'autor en altres treballs. Abstract-Big Data has become commonplace in most Internet-based applications, which by delivering services to planetary scale numbers of users generate very large data sets. Such data sets are considered as a valuable source of analytics information and knowledge for many purposes and domains. It is claimed each time more that Big Data and machine learning, especially data mining, are the basis for developing advanced analytics platforms for turning data into valuable assets, gaining competitive advantage and make better decisions. At the same time, however, Big Data applications are showing to be killer applications for the state of the art machine learning and data mining algorithms. Indeed, traditional data mining frameworks such as WEKA, R, etc. and those from big companies such as IBM SPSS Modeler, SAS Enterprise Miner, Oracle Data Mining, etc. are facing the challenges of 1) coping with mining large data sets within short times and 2) under high rates of data generation. The way envisaged ahead to effectively deal with such challenges is to move to Cloudbased versions of such frameworks and development of new frameworks implemented using Cloud platforms. In either case, data mining and machine learning algorithms are being fully implemented in Cloud platforms under new requirements of Big Data for efficiency and performance. In the group of newly developed frameworks there is Apache Mahout, whose goal is "to build an environment for quickly creating scalable performant machine learning applications". In this paper we analyse the performance of some clustering algorithms of Apache Mahout using a Twitter streaming dataset under a Hadoop MapReduce cluster infrastructure according to various evaluation criteria.

show abstract

Mining Navigation Patterns in a Virtual Campus

Cited by 7 publications

References 7 publications

Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluation

Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluation

Distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints

Performance Evaluation of Mahout Clustering Algorithms Using a Twitter Streaming Dataset

Contact Info

Product

Resources

About