Crowdsourcing allows large-scale and flexible invocation of human input for data gathering and analysis, which introduces a new paradigm of data mining process. Traditional data mining methods often require the experts in analytic domains to annotate the data. However, it is expensive and usually takes a long time. Crowdsourcing enables the use of heterogeneous background knowledge from volunte...

In the age of big data, information mining technology has undergone tremendous change; traditional forecasting mining technology has not been able to solve the information mining problems under a large scale of data. this paper put forward a modeling mechanism of information analysis and mining under the age of big data, the modeling mechanism is, first, construct the model of task decompositio...

In this paper Present survey on Data mining, Data mining using Rough set Theory and Data Mining using parallel method for rough set Approximation with MapReduce Technique. With the development of Information technology data growing at a tremendous rate, so big data mining and knowledge discovery become a new challenge. Rough set theory has been successfully applied in data mining by using MapRe...

Data Mining techniques have been applied in many application areas. A Data Mining project has been often described as a process of automatic discovery of new knowledge from large amounts of data. However the role of the domain knowledge in this process and the forms that this can take, is an issue that has been given little attention so far. Based on our experience with a large scale Data Minin...

Frequent pattern mining is an essential data mining task, with a goal of discovering knowledge in the form of repeated patterns. Many efficient pattern mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data we are presented with today, the so-called “Big Data”. Scalable parallel algorithms hold the key to solving the problem in this context. In...

Sequential Association Rule Mining (ARM) algorithms are characterized by a high computational complexity due to two facts: (i) they have to mine a very large search space (ii)they have high demands of database access. Association rule mining technique have progressively been adapted to large-scale systems in order to benefit from the large-scale computing capabilities and the huge storage capac...

In recent years the developments and opportunities created for e-Science infrastructure have promised technological support for the ever growing area of text mining applications and services. The computationally expensive tools have previously only been usable on small scale systems but are now being developed for much larger scale tasks thanks to alternative models of processing and storage. I...

The problem of “scaling up for high dimensional data and high speed data streams” is among the “ten challenging problems in data mining research”[36]. This paper is devoted to estimating entropy of data streams. Mining data streams[19, 4, 1, 29] in (e.g.,) 100 TB scale databases has become an important area of research, e.g., [10, 1], as network data can easily reach that scale[36]. Search engi...

One of the most important problems in data mining is discovery of association rules in large database. We had proposed parallel algorithms for mining generalized association rules with classi cation hierarchy. In this paper, we implemented the proposed algorithms on a large scale PC cluster which consists of one hundred PCs interconnected by an ATM switch, and analyzed the performance of our al...

This paper discusses the design and deployment of low-cost Internet Things (IoT) in medium-scale open pit mines to optimise performance their mining small-scale trucks surface shovels. Low-cost IoT can be implemented operations automate collection process management information that is currently measured manually, replicating part results delivered by commercial Fleet Management Systems (FMSs) ...