ecotoxicity of rhodium (rh) from the model of mining waste water side stream was examinedin this paper. rh was extracted from the model of the mining waste water using an emulsion liquid membrane(elm). the extractions were done at ph of 1.87 and ph 2.92 and 41.6 % and 46.2 % of rh was extractedrespectively. the side streams of ph of 1.87 and ph 2.92 after the extractions were examined for ecoto...

With the usefulness of data mining in various fields of information science, various mining methods have been proposed in previous research. Recently, in these fields, data has taken the form of continuous data streams rather than finite stored data sets. In this paper, a mining method of sequential patterns over an online sequence data stream is proposed, which is useful for retrieving embedde...

In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds. In [20], a framework for mining frequent itemsets over a data stream is proposed by the use of weighted slide window model. Two algorithms of single pass (WSW) and the WSW-Imp (improving one) using weighted sliding model were proposed in there to solve th...

Data stream mining has attracted much research attention from the data mining community. With the advance of wireless networks and mobile devices, the concept of ubiquitous data mining has been proposed. However, mobile devices are resource-constrained, which makes data stream mining a greater challenge. In this paper, we propose the RA-HCluster algorithm that can be used in mobile devices for ...

The nature of data on the Web is becoming more and more streamoriented and in this context, the idea of mining Web-generated data streams is becoming a hot topic. Web services, on the other hand, have become an inevitable tool for the future development of the Web. While Web services have been very successful in providing distributed computing environments, they have not been exploited for buil...

Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches assume a fixed support threshold, a changeable threshold is more realistic, considering the rapid updating of the streaming transactions in practice. Additionally, mining of itemsets over various time granularities rathe...

We will demonstrate the visual analytics system V istream , that supports interactive mining of complex patterns within and across live data streams and stream pattern archives. Our system is equipped with both computational pattern mining and visualization techniques, which allow it to not only efficiently discover and manage patterns but also effectively convey the mining results to human ana...

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 sol...

Data mining is a part of a process called KDD-knowledge discovery in databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation. Association rule techniques are used for data mining if the goal is to detect relationships or associations between specific values of categoric...