Tetkik: Akan veri kümeleme algoritmalarını çalıştırma ve karşılaştırma

12th Turkish National Software Engineering Symposium, UYMS 2018; Istanbul; Turkey; 10 September 2018 through 12 September 2018Recently, clustering data streams have become an incredibly important research area for knowledge discovery as applications produce more and more unstoppable streaming data. In this paper we introduce clustering, streams and data streaming clustering algorithms, as well as discussions of the most important stream clustering algorithms, considering their structure. As an additional contribution of our work and differently from review and survey papers in stream clustering, we offer the practical part of the most known stream clustering algorithms, namely: (i) CluStream; (ii) DenStream; (iii) D-Stream; and (iv) ClusTree, showing their experimental results along with some performance metrics computation of for each, depending on MOA framework.Son zamanlarda, veri akışlarını kümelemek uygulamalar daha fazla durdurulamaz veri akışı üretirken bilgi keşfi için inanılmaz derecede önemli bir araştırma alanı haline gelmiştir.Bu makalede, kümeleme, akışlar ve veri akışlarını kümeleme algoritmalarını en önemli akım kümeleme algoritmalarının irdelenmesini yapılarını da göz önünde bulundurarak tanıtıyoruz. Çalışmamızın ek bir katkısı ve akış kümeleme alanında yapılmış tetkit ve gözden geçirme makalelerinden farklı olarak en bilinen akış kümeleme algoritmalarının Pratik kısmını, yani: (i) CluStream; (ii) DenStream; (iii) D-Stream; and (iv) ClusTree, MOA Java çerçevesine bağlı olarak, her biri için bazı performans metriklerinin hesaplanmasıyla birlikte deney sonuçlarını göstererek sunuyoruz

When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events

Predicting both the time and the location of human movements is valuable but challenging for a variety of applications. To address this problem, we propose an approach considering both the periodicity and the sociality of human movements. We first define a new concept, Social Spatial-Temporal Event (SSTE), to represent social interactions among people. For the time prediction, we characterise the temporal dynamics of SSTEs with an ARMA (AutoRegressive Moving Average) model. To dynamically capture the SSTE kinetics, we propose a Kalman Filter based learning algorithm to learn and incrementally update the ARMA model as a new observation becomes available. For the location prediction, we propose a ranking model where the periodicity and the sociality of human movements are simultaneously taken into consideration for improving the prediction accuracy. Extensive experiments conducted on real data sets validate our proposed approach

Data Stream Clustering: Challenges and Issues

Very large databases are required to store massive amounts of data that are continuously inserted and queried. Analyzing huge data sets and extracting valuable pattern in many applications are interesting for researchers. We can identify two main groups of techniques for huge data bases mining. One group refers to streaming data and applies mining techniques whereas second group attempts to solve this problem directly with efficient algorithms. Recently many researchers have focused on data stream as an efficient strategy against huge data base mining instead of mining on entire data base. The main problem in data stream mining means evolving data is more difficult to detect in this techniques therefore unsupervised methods should be applied. However, clustering techniques can lead us to discover hidden information. In this survey, we try to clarify: first, the different problem definitions related to data stream clustering in general; second, the specific difficulties encountered in this field of research; third, the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and how several prominent solutions tackle different problems. Index Terms- Data Stream, Clustering, K-Means, Concept driftComment: IMECS201