The crime of illegal nickel ore mining is a in the business carried out by individuals, groups people, or companies/foundations with legal entities which their operations do not have permit from government agencies based on applicable regulations threatened criminal sanctions for anyone who violates prohibition. Article 38 Law Number 3 2020 concerning Mineral and Coal Mining states that “Mining...

Over the last decade process mining techniques have matured and more and more organizations started to use process mining to analyze their operational processes. The current hype around “big data” illustrates the desire to analyze ever-growing data sets. Process mining starts from event logs—multisets of traces (sequences of events)—and for the widespread application of process mining it is vit...

An important aspect of data mining algorithms and systems is that they should scale well to large databases A consequence of this is that most data mining tools are based on machine learning algorithms that work on data in attribute-value format. Experience has proven that such ’single-table’ mining algorithms indeed scale well. The downside of this format is, however, that more complex pattern...

Objective This work leverages spatio-temporal data mining (ST-DM), the MiSTIC (Mining Spatio-Temporally Invariant Cores)[1,6] method for infectious disease surveillance, by identifying a) Extent of spatial spread of disease core regions across populations-scale of disease prevalence b) Possible causes of the observed patterns-for better prediction, detection & management of infectious disease &...

In order to improve the accuracy of data-mining in large-scale complex networks, an incremental subspace data-mining algorithm based on data-flow density of complex network is proposed in this paper. In order to accommodate this goal, a latent variable model is first introduced and incorporated into Data-flow density model so that the network is divided into different communities and the defect...

Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. Mining frequent patterns from large scale databases has emerged as an important problem in data mining and knowledge discovery community. A number of algorithms has been proposed to determine frequent pattern. Apriori algorithm is the first algorithm proposed in this field. With the...

It is an important part of research content in data mining to discover association rules from large scale database, the main problem of which is frequent itemsets mining. The classical Apriori algorithm is an efficient one for that. Aimed at the performance bottlenecks of multiply scanning the database and generating a large quantity of candidate itemsets in Apriori algorithm, an improved algor...

Large-scale spatio-temporal data is crucial for testing the spatio-temporal pattern mining algorithms. In this paper we present a data set generator that can generate moving spatio-temporal instances, with evolving regions. The characteristics of data generated can be configured with parameters given to program and those parameters allow users to test the performance of spatio-temporal mining a...

Natural disasters cause large-scale network service interruption which corresponds to unreachability of networks. This problem relates to how networks respond under extreme conditions, and it is neither well-studied nor well-understood. To infer network service disruption, challenges arise, i.e., how to use heterogeneous data that include sensory measurements and human inputs? This work shows a...

With the advance of computing power, data mining techniques can extract useful information from large amount of data. In 2012, 2.5 quintillion bytes of data (1 follow 18 zeroes) are created every day. Data privacy is of utmost concern for distributed data mining across multiple parties, which may be competitors. In this thesis, we focus on the privacy preserving techniques in distributed data m...