A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clus...

We consider the problem of detecting changes in a multivariate data stream. A change detector is defined by a detection algorithm and an alarm threshold. A detection algorithm maps the stream of input vectors into a univariate detection stream. The detector signals a change when the detection stream exceeds the chosen alarm threshold. We consider two aspects of the problem: (1) setting the alar...

We consider the problem of maintaining aggregates over recent elements of a massive data stream. Motivated by applications involving network data, we consider asynchronous data streams, where the observed order of data may be different from the order in which the data was generated. The set of recent elements is modeled as a sliding timestamp window of the stream, whose elements are changing co...

In this paper, an innovative technique called Relational Time-Space Data Structures is presented for Conflict Detection (CD) between 4D trajectories, improving the performance of the widely-used pairwise CD algorithms and also overcoming some of the shortages of previous Spatial Data Structures CD algorithm. WP-E project STREAM aims to coordinate the whole European ATM at strategic and tactical...

clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. the aim of clustering is to collect data points. in this paper, a cultural algorithm (ca) is presented to optimize partition with n objects into k clusters. the ca is one of the effective methods for searching into the problem space in order to find a n...

With China's rapid development of e-commerce and logistics, many large electronic business enterprises start to establish large volume warehouses. It takes a long time to distribute the goods every time, so the optimal distribution link can save a lot of time and it has important practical significance. In order to optimize goods inventory and delivery, the electronic commerce goods shopping ca...

Developments in data streams, coupled with the growth in mobile and pervasive devices, have led to the emergence of Ubiquitous Data Mining (UDM). UDM aims to perform data stream mining in a ubiquitous environment with resourceconstrained and/or mobile devices. Over the past few years, stream mining techniques have attracted the attention of the data mining community. However these techniques ha...

We study the 2-center problem with outliers in highdimensional data streams. Given a stream of points in arbitrary d dimensions, the goal is to find two congruent balls of minimum radius covering all but z points. We provide a (1.8 + ε)-approximation streaming algorithm for the problem, improving upon the previous (4 + ε)approximation algorithm available for the problem. The space complexity an...

We study the 2-center problem with outliers in high-dimensional data streams. Given a stream of points in arbitrary d dimensions, the goal is to find two congruent balls of minimum radius covering all but at most z points. We present a (1.8 + ε)approximation streaming algorithm, improving over the previous (4+ε)-approximation algorithm available for the problem. The space complexity and update ...

In this paper, we propose an algorithm for computing and maintaining recently frequent patterns which is more stable and smaller than the data stream and dynamically updating them with the incoming transactions. Our study mainly has two contributions. First, a regression-based data stream model is proposed to differentiate new and old transactions. The novel model reflects transactions into man...