Algorithms for clustering clickstream data

Algorithms for clustering clickstream data

Clustering is a classic problem in the machine learning and pattern recognition area, however a few complications arise when we try to transfer proposed solutions in the data stream model. Recently there have been proposed new algorithms for the basic clustering problem for massive data sets that produce an approximate solution using efficiently the memory, which is the most critical resource f...

A Model for Clustering Clickstream Data

The extremely large number of data sets that can be drawn from internet has bootstrapped in a way the data mining techniques from extracting real time results. For this reason clustering and other mining techniques in the data stream model have grasped the interest of Data Mining community [6]. Recently have been proposed many algorithms for the basic clustering problem for massive data sets [7...

Concept-based Clustering of Clickstream Data

A Comparative Study of Some Clustering Algorithms on Shape Data

Recently, some statistical studies have been done using the shape data. One of these studies is clustering shape data, which is the main topic of this paper. We are going to study some clustering algorithms on shape data and then introduce the best algorithm based on accuracy, speed, and scalability criteria. In addition, we propose a method for representing the shape data that facilitates and ...

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...