Mining Problem Research Articles

Carbon Emission Forecasting Study Based on Influence Factor Mining and Mini-Batch Stochastic Gradient Optimization

With the increasing prominence of the global carbon emission problem, the accurate prediction of carbon emissions has become an increasingly urgent need. Existing carbon emission prediction methods have the problems of slow calculation speed, inaccurate prediction, and insufficient deep mining of influencing factors when dealing with large-scale data. In this study, a comprehensive carbon emission prediction method is proposed. Firstly, multiple influencing factors including economic factors and demographic factors are considered, and a pathway analysis method is introduced to mine the long-term relationship between these factors and carbon emissions. Then, indirect influence terms are added to the multiple regression equation, and the variable is used to represent the indirect influence relationship. Finally, this study proposes the PCA-PA-MBGD method, which applies the results of principal component analysis to the pathway analysis. By reducing the data dimensions and extracting the main influencing factors, and optimizing the carbon emission prediction model by using a mini-batch stochastic gradient descent algorithm, the results show that this method can process a large amount of data quickly and efficiently, and realize an accurate prediction of carbon emissions. This provides strong support for solving the carbon emission problem and offers new ideas and methods for future related research.

ABUS tumor segmentation via decouple contrastive knowledge distillation

Objective. In recent years, deep learning-based methods have become the mainstream for medical image segmentation. Accurate segmentation of automated breast ultrasound (ABUS) tumor plays an essential role in computer-aided diagnosis. Existing deep learning models typically require a large number of computations and parameters. Approach. Aiming at this problem, we propose a novel knowledge distillation method for ABUS tumor segmentation. The tumor or non-tumor regions from different cases tend to have similar representations in the feature space. Based on this, we propose to decouple features into positive (tumor) and negative (non-tumor) pairs and design a decoupled contrastive learning method. The contrastive loss is utilized to force the student network to mimic the tumor or non-tumor features of the teacher network. In addition, we designed a ranking loss function based on ranking the distance metric in the feature space to address the problem of hard-negative mining in medical image segmentation. Main results. The effectiveness of our knowledge distillation method is evaluated on the private ABUS dataset and a public hippocampus dataset. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in ABUS tumor segmentation. Notably, after distilling knowledge from the teacher network (3D U-Net), the Dice similarity coefficient (DSC) of the student network (small 3D U-Net) is improved by 7%. Moreover, the DSC of the student network (3D HR-Net) reaches 0.780, which is very close to that of the teacher network, while their parameters are only 6.8% and 12.1% of 3D U-Net, respectively. Significance. This research introduces a novel knowledge distillation method for ABUS tumor segmentation, significantly reducing computational demands while achieving state-of-the-art performance. The method promises enhanced accuracy and feasibility for computer-aided diagnosis in diverse imaging scenarios.

Khai thác dữ liệu chuỗi theo mối quan tâm của người dùng

Sequence data mining, also known as sequential pattern mining, is to find all frequent sub-sequences (called sequential patterns) in a sequence database, the threshold of frequency is specified by the user. In recent years, with the explosion growth of information and big data, this problem trends toward mining with constraints to overcome both effectiveness and efficiency challenges since that the constraints represent for the user’s interest. This paper presents a detailed survey of recent studies on mining sequential pattern and the categories of the constraints. Moreover, it also classifies the main methods and analyzes their advantages and limitations, thereby main approaches and strategies to solve sequential pattern mining problems in the future are presented.

Towards a Circular Economy of Lithium-Ion Batteries – Is a Fast Ramp-up of Recycling and Circularity Sustainable?

With the rapidly growing demand for lithium-ion batteries (LIBs) in electric vehicles (EV) major global challenges for policymakers and companies arise. Global production and supply chains of battery materials increasingly account for environmental issues accompanied by social problems of raw material mining (e.g., for cobalt in DR Congo) and recent highly fluctuating raw materials prices. To face these problems, policymakers and companies are pushing for circular economy strategies for battery supply chains to guarantee a sustainable supply of critical raw materials such as lithium, cobalt, and nickel, and to integrate used batteries back into the value chain by recycling or reuse. But reaching a full circular economy early does not translate into improved sustainability at the same time. A circular economy of batteries in EVs is fully developed once the demand for raw materials in EV batteries is completely covered by recycled materials, the break-even points (BEPs) of a circular economy. At this stage, regional independence from primary raw materials with critical production and supply chains is achieved.In this study, a critical perspective on the pushing for strategies to reach circular economy of EV batteries early is taken. Within three steps it is shown, (1) when the circular economy is realized on a regional level by reaching BEPs, (2) how to accelerate reaching these individual BEPs for battery materials through different scenarios, and (3) what this acceleration scenarios implies in terms of a sustainable global production of battery materials via mining and transportation compared to reuse and recycling.In the first step, this numerical analysis focuses on identifying the BEPs of lithium, cobalt, and nickel for EV batteries in key regions, namely Europe, the US and China, by using a dynamic material flow analysis (MFA). For this purpose, the life cycle of a battery is considered and analyzed in a circular economy approach via production, EV life, secondary application (re-use) and recycling. The results show that China is the first region to achieve BEPs for Co and Ni in the 2040s while focusing on LFP as the main battery technology in the upcoming decades, followed by Europe and the U.S. Compared to China, EV battery technologies in Europe and the USA are dominated by NMC and NCA leading to later BEPs for cobalt and nickel. Generally, cobalt will be the first metal to achieve BEPs, while the secondary coverage of lithium poses a major future global challenge.Second, based on a sensitivity analysis of the model parameters with the highest impact on the BEPs, different scenarios of parameter adaptions are presented which lead to potential earlier achievement of the BEPs in Europe, the USA and China (shift scenarios). These scenarios include early full electrification of car sales, no secondary application of EV batteries, reduction of EV battery sizes in time, shorter lifespan of batteries in EVs and high production scrap rates. The results show that each of the five shift scenarios has a different impact on accelerating reaching the BEPs in each region for the individual raw materials.Third, in each shift scenario, the necessary mining and production amount of primary EV battery materials until reaching the BEPs are analyzed. Although achieving BEPs earlier by applying the shift scenarios seems beneficial, the results indicate that more primary raw materials must be produced annually until reaching the BEP under certain conditions, suggesting that more social and environmental problems linked to raw material mining and transportation would have to be tolerated. Therefore, a sustainable slow ramp-up of mines and recycling must be weighted individually against a fast ramp-up and, thus, against reaching a full circular economy early.This analysis and the model in general can help policymakers and companies to strategically decide whether and how to accelerate achieving certain BEPs for an early independence from primary raw materials. However, by taking a critical perspective here, it is shown that accelerating the transition towards circular economy by reuse and recycling can be accompanied by intensifying temporary sustainable issues of raw material mining and transportation.

Sustainable Environmental Management and Preservation in Old Well Oil and Gas Traditional Mining Sites Indonesia: Whose Responsibility?

Objective: Environmental pollution and destruction at traditional oil and gas mining sites with old wells in Indonesia is truly worrying, and poses a serious threat to present and future generation safety. Sustainable management and conservation is no longer an option, but must be an inevitability and cannot be postponed any longer. Methods: This research uses a dogmatic legal research method (juridical normative) which was supported by field research, and in-depth interviews with parties who are directly involved, and understand the problems in the Pertamina EP Asset IV Field Cepu Working Area, Bojonegoro Regency, East Java, which is quite representative of all mining problems traditional old well oil and gas in Indonesia. Results: Pertamina EP, Perum Perhutani, Bojonegoro Regency, and East Java Province, each as territory owner, feel that they have no authority and no responsibility (evade) to issue policies (beleid), carry out management (bestuursdaad), regulations (regelendaad), management (beheersdaad), supervision (toezichthoudensdaad), and enforcing the law, so that there is an impression of negligence which results in increasingly severe environmental pollution and destruction. Based on the facts, it turns out that each institution has obtained quite large benefits, and advantages, based on applicable laws, and regulations, each also has opportunity (rights, and obligations) to save the environment. However, due to the strong sectoral egoism in each institution, environmental pollution, and damage problem was neglected. Conclusion: This research is to find a solution, namely a cooperation agreement (MoU) to carry out rescue work. The research results show that the best solution is that each institution must realize that they have the same obligations and responsibilities, join hands, close ranks and put aside sectoral egoism to overcome environmental damage at old wells oil and gas mining sites without getting worse.

Research on reasonable layout parameters of working faces based on the concept of harmonic extraction to reduce the damage of coal seam waiting for upward mining

Based on the idea of harmonic extraction, the problem of reducing the damage of the coal seam waiting for upward mining. Using the theoretical analysis method, a schematic diagram of coordinated mining along the dip direction of coal seam is established, and the calculation method of reasonable layout parameters of coal seam working face early mining is given. Based on the upward mining problem of the No.1 coal seam in the fifth panel of Zaoquan Coal Mine, the influence parameters of the No.2 coal seam, No.6 coal seam and No.7 coal seam mining on the No.1 coal seam were determined by similar simulation test. Then, the layout parameters of working face were determined. The research method of numerical calculation was used to evaluate the degree and uniformity of movement and deformation of the No.1 coal seam, combining five indicators: subsidence, horizontal movement, inclined deformation, curvature deformation, and horizontal deformation. The results indicate that when the working face is arranged using the layout parameters provided in this article, it can promote the further subsidence of the No.1 coal seam at the position of the remaining coal pillar. The movement and deformation indicators of the No.1 coal seam all reach the most uniform degree of the geological mining conditions, which can effectively offset the uneven deformation problem of the No.1 coal seam caused by the influence of the remaining coal pillars, making multiple mining operations a favorable condition for upward mining and achieving the goal of reducing the damage of the No.1 coal seam.

Experimental Study of the Multiple Fractalisation of Coal and Rock Failure Subjected to the Coupled Effects of Water, Temperature and Dynamic Loads

The mechanical properties of water-rich coal and rock in a subzero environment are very different from those at room temperature, which causes many unexpected hazards for projects. In this study, coal and rock samples subjected to the coupled effects of water, temperature, and dynamic loads were taken as the research object, and the discussion was shaped around their mechanical properties. The crack evolution trend and different gradient impact velocities were determined using a split-Hopkinson pressure bar (SHPB). Multiple fractals were based on high-speed digital image correlation (HS-DIC) technology and the quality-screening method; the evolution trend of the surface cracks in the crushing process and the distribution characteristics of the specimen fragments after crushing were examined from the perspective of fractals. This provided a powerful supplement to the existing research system on the problem of mining via the freezing method, and it accounted for the shortcomings of the existing research to a certain extent. In this research, the results mainly showed four points: (1) The coal samples were determined to have a wave velocity between 1.68 and 2.01 km/s, while the rock samples were between 2.24 and 2.61 km/s. Under the same conditions, the rock’s resistance to deformation and damage was greater than that of coal. (2) In the saturated state, the plastic strength of the coal and rock samples was greater than that in the dry state, due to the strengthening of their internal stresses caused by the presence of fissure water. (3) With decreasing temperature, the degree of the dynamic compression factor of coal and rock showed a trend of initially increasing, then decreasing, and then increasing. With the increase in the loading rate, the destruction of the coal and rock was more intense, and the destruction process was accelerated. (4) After the saturated coal and rock samples were frozen, their interiors were affected by the dual factors of contraction under the influence of temperature and expansion under the influence of the freezing expansion force. The internal fissures closed or shrank, and the water in the pores turned into ice, leading to an increase in pore volume.

Anti-Money Laundering by Group-Aware Deep Graph Learning

Anti-money laundering (AML) is a classical data mining problem in finance applications. As well known, money laundering (ML) is critical to the effective operation of transnational and organized crime, which affects a country's economy, government, and social wellbeings. Financial services organizations facilitate the movement of money and have been enlisted by governments to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development. In the application of AML, user identity and financial behavior data are widely used to detect laundering transactions. In recent years, an increasing number of money laundering activities have been conducted by organized criminal gangs while most existing works still treat the actions of each account as independent identity behavior without considering the group-level conspired interactions. Therefore, in this paper, we propose a group-aware deep graph learning-based approach for organized money-laundering detection. In particular, we design a community-centric encoder to represent the nodes and attributes in user transaction graphs and derive the adjacent gang behaviors. Then, we devise a scheme of local enhancement to accommodate nodes with similar transaction features, which are aggregated into gangs for downstream detection. Extensive experiments on the real-world dataset from one of the largest bank card alliances worldwide show that our proposed method outperforms state-of-the-art methods in both offline and online modes, showing the effectiveness of money laundering detection with group-aware deep graph learning.

A new tree-based approach to mine sequential patterns

Generic sequential pattern mining problem aims to mine the set of sequential patterns from a sequential database that satisfies a minimum support or occurrence threshold constraint. The main challenges that affect the efficiency of a solution lie in reducing the pattern search space, early detecting the infrequent patterns, representing the database in an efficient format, etc. Also, additional challenges get included when the problem environment transitions from static to incremental database leading to not to re-mine but efficiently tracking the effect of the incremental portion over the complete updated database. In this article, we introduce a new tree-based solution to the sequential pattern mining problem, including two sets of novel solutions for static and incremental sequential databases. We propose two new structures, SP-Tree and IncSP-Tree, and design two efficient algorithms, Tree-Miner and IncTree-Miner to mine the complete set of sequential patterns from static and incremental databases respectively. The proposed novel structures provide an efficient manner to store the complete sequential database maintaining “build-once-mine-many” property and giving scope to perform interactive mining. Additionally, we also design a new breath-first based support counting technique to efficiently identify the infrequent patterns at early stages and a new heuristic pruning strategy to reduce pattern search space. We also design a new pattern storage structure BPFSP-Tree to store the frequent patterns during successive iterations in incremental mining to reduce the number of database scans and to remove the infrequent patterns efficiently. A novel structure named Sequence Summarizer is also introduced to efficiently calculate and update the co-occurrence information of the items, especially in an incremental environment. Experimental results from various real-life and synthetic datasets demonstrate the efficiency of our work in comparison with the related state-of-the-art approaches.

Performance Analysis of Sketching Methods

One of the problems in data mining or machine learning is the high-dimensional dataset. MinHash can generate sketches of sparse datasets efficiently reducing the dimension to a few thousand. These sketches, then, can be used for different machine-learning applications. It takes O(kd) computations to generate k hash values for a data point with d non-zeros. The Sketch of a data point is the vector of k hash values. Weighted Minwise Hashing is another method to generate a sketch of size k in O(kp/d) computations. Here, p is the size of the universal set. Optimal Densification is the most efficient and accurate method as it can generate k hash values in mere O(d + k) computations. In this paper, we investigate the performance of Optimal Densification, Weighted Minwise Hashing, and Vanilla MinHash by performing two experiments. Firstly, we investigate Jaccard similarity estimation accuracy on six different synthetic datasets. Then, we perform one nearest neighbor classification (1NN) of four real datasets. Optimal Densification outperforms both Weighted Minwise Hashing and Vanilla MinHash in terms of accuracy and time taken to generate the sketches.

All the world’s a (hyper)graph: A data drama

Abstract We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare’s plays. Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication settings and character contributions as hyperedges with edge-specific node weights. By making multiple intuitive representations readily available for experimentation, we facilitate rigorous representation robustness checks in graph learning, graph mining, and network analysis, highlighting the advantages and drawbacks of specific representations. Leveraging the data released in Hyperbard, we demonstrate that many solutions to popular graph mining problems are highly dependent on the representation choice, thus calling current graph curation practices into question. As an homage to our data source, and asserting that science can also be art, we present our points in the form of a play.1

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.

Development and application of air-coupled GPR antenna for coal-rock interface detection in mines

Rapid and high precision detection of coal-rock interface is one of the key problems in intelligent coal mining. In this paper, an air-coupled ground penetrating radar (GPR) antenna is developed for coal-rock interface detection in fully mechanized mining face, which can realize non-contacting, high precision, high-resolution detection during the mining process. The main features of the air-coupled GPR antenna are ultra-wideband, narrow beam, and high gain. For the vertical polarization pattern at 1200 MHz, the main lobe amplitude is 6.29dBi and the main lobe width (3 dB) is 108.3°, as well as the relative bandwidth of the antenna can reach 51%. For design validations, the novel air-coupled GPR antenna developed in this paper is tested in mine, the detection error and the average error percentage are ±1.2 cm and 8.6%, respectively, which shows the superior detection ability of the novel air-coupled GPR antenna and the applicability in complex geological environment, providing technical support for the identification of coal-rock interface in intelligent coal mining.

Research on New Greenable Class Gravity Retaining Wall Structure Technology Based on Video Monitoring

As the most common geological disaster problem in mines, slope geological disasters have become a focus of research, along with the difficulty of mine safety and ecological environment protection together with the ecological restoration of open-pit mines. At present, a large number of slope-retaining wall structures lack research on safety monitoring, real-time acquisition, and intelligent early warning. Therefore, this paper combines cement-modified loess with gravity retaining wall structures and puts forward a new type of greening gravity retaining wall structure. From the perspective of “the Internet of Things + construction”, a video monitoring system is established to monitor the retaining wall structure in real time. Finally, based on video image processing technology, the deformation of the retaining wall surface is identified and the inclination angle of the wall surface is calculated, so as to improve the real-time and intelligent monitoring of the new greening gravity retaining wall. The results show that the new greening gravity retaining wall based on video monitoring proposed in this paper has the characteristics of a gravity retaining wall and ecological retaining wall, which are conducive to improving the real-time and intelligent monitoring of the new greening gravity retaining wall. Cement-modified loess is used as the planting matrix, and the cement mixing ratio should not exceed 10%. Considering the requirements of economy and shear strength, the cement mixing ratio should be selected from 5% to 12%.

Simulation test study on filling flow law of gangue slurry in goaf

The disposal and utilization of solid waste of coal gangue is one of the main problems in coal mining in China. Injecting coal gangue into goaf in the form of slurry can effectively solve the problems of ground stacking and environmental pollution prevention. In order to obtain the flow law of gangue slurry in the void of the accumulated rock in the goaf, a visualization simulation test device for gangue slurry permeation grouting in the goaf was independently designed. The flow and diffusion characteristics, flow and diffusion velocity changes, void pressure changes, and viscosity changes of three mass concentrations (76%, 78%, 80%) of gangue slurry in the void between caved rock blocks in goaf were studied by visual grouting simulation test. The results show that: (1) The seepage process of gangue slurry in the goaf simulation test is divided into three diffusion forms, namely radial diffusion, axial diffusion, and bidirectional diffusion. The three diffusion forms are interrelated and inseparable. (2) The initial flow velocity of the slurry with different concentrations is different under the same permeation grouting pressure, and the higher the slurry concentration, the smaller the initial flow velocity of the slurry. The velocity of the slurry has a nonlinear relationship with the diffusion distance of the slurry. (3) With the permeation and diffusion of slurry, pressure sensors at different positions are subjected to pressure from bottom to top and enter the pressure boost stage, gradually forming stress peaks. When the slurry exceeds the position of the pressure sensor, the pressure on the pressure sensor is weakened and begins to enter the pressure relief stage, and the stress decline trend gradually becomes gentle with time. (4) The water loss effect occurs during slurry flow interaction with rock mass, resulting in slurry viscosity increasing. The viscosity of the slurry affects the difference in the amount of viscosity change. The research results can provide a certain theoretical basis for the goaf gangue slurry filling project.

Consistent Matrix: A Feature Selection Framework for Large-Scale Datasets

Large-scale data processing based on limited computing resources has always been a difficult problem in data mining, where feature selection is often used as an effective data compressing mechanism. For granular computing of big data, discernibility matrix and dependency degree are the most representative methods for matrix-based and feature importance degree-based feature selection, respectively. However, their temporal and space complexities are high and often lead to poor performance. In this paper, a novel feature selection framework for large scale data processing with linear complexities was proposed for the first time. Firstly, a much more concise fuzzy granule set, called fuzzy arithmetic covering, was introduced to reduce computational costs. Then a new matrix-based feature selection framework, namely consistent matrix, was proposed for general rough set models. As a result, a heuristic attribute reduction algorithm, i.e., HARCM, was designed accordingly. Compared with six state-of-the-art algorithms for feature selection, the average running time of the newly proposed algorithm was reduced up to 2913 times, with a comparable or even better classification performance.

Penegakan Hukum Terhadap Pelaku Pertambangan Timah Illegal di Kawasan Lintas Timur Dusun Tanjung Ratu Kabupaten Bangka

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p class="AbstractText">Community mining, as a means of fulfilling daily needs through the utilization of existing mining resources, is a prominent activity in various regions. This study delves into the specific issues surrounding mining practices within the East Cross area of Tanjung Ratu Hamlet in Bangka Regency. It critically examines the driving forces and factors enticing individuals to engage in this illegal tin mining activity while also assessing the effectiveness of law enforcement measures taken against such actors. Employing an empirical juridical approach, this research relies on field investigations to comprehend the on-ground realities and conditions. The study reveals a persistent trend of illegal tin mining activities being conducted by the local community in the Lintas Timur area of Dusun Tanjung Ratu, Kabupaten Bangka, dating back to the early stages of reform. Intriguingly, these illicit operations have extended beyond land boundaries into water territories, in direct contravention of Perda No. 3 of 2020, which designates the area as a zero mining zone. Consequently, those involved in mining activities within this zone are classified as illegal tin miners. Despite concerted law enforcement efforts, including police raids and other enforcement actions, the study exposes a glaring inefficacy in curbing these illicit mining activities, as many continue to operate even after such interventions. The study's primary objective is to shed light on the enforcement mechanisms employed against illegal tin mining in the Lintas Timur area of Dusun Tanjung Ratu, Kabupaten Bangka, while also highlighting the challenges encountered. Ultimately, it aims to offer policy recommendations to local governments for formulating more effective strategies to combat the persistent problem of illegal tin mining in Bangka Belitung.</p></td></tr></tbody></table></div>

Targeted mining of contiguous sequential patterns

In recent years, sequential pattern mining (SPM) has been applied in many domains, including recommender systems, fraud detection, and other related industries. Target-oriented SPM (TSPM) has been proposed to improve the interpretability of SPM by addressing the issue that too many irrelevant or meaningless sequential patterns are often discovered, which facilitates data analysis. Nevertheless, current TSPM research does not consider contiguity to identify sequential patterns, despite the fact that it is an important feature of sequence data. Contiguity has significant implications in applications such as bioinformatics and network intrusion detection, as it can aid the detection of periodic patterns and anomalous behaviors. Therefore, this paper proposes a novel problem of target-oriented contiguous sequential pattern mining (TCSPM) and introduces two algorithms, called TCSPM and TCSPM+. TCSPM utilizes a compact data structure called sequence chain and relies on a sequence chain pruning strategy to reduce the search space. In addition, TCSPM applies a reverse matching technique to quickly search for targeted contiguous sequences. TCSPM+ is an optimized version of TCSPM that utilizes sequence segmentation operations and a query sequence pruning strategy to further improve performance. Extensive experiments on both real and synthetic datasets demonstrate that TCSPM is an efficient algorithm for discovering targeted contiguous sequential patterns (TCSPs). TCSPM+ substantially enhances the performance of TCSPM in terms of runtime, memory usage, and scalability, outperforming TCSPM by an order of magnitude on some datasets.

Discovering Interesting Patterns from Hypergraphs

A hypergraph is a complex data structure capable of expressing associations among any number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are useful to model real-life problems. Frequent pattern mining is one of the most popular problems in data mining with a lot of applications. To the best of our knowledge, there exists no flexible pattern mining framework for hypergraph databases decomposing associations among data entities. In this article, we propose a flexible and complete framework for mining frequent patterns from a collection of hypergraphs. To discover more interesting patterns beyond the traditional frequent patterns, we propose frameworks for weighted and uncertain hypergraph mining also. We develop three algorithms for mining frequent, weighted, and uncertain hypergraph patterns efficiently by introducing a canonical labeling technique for isomorphic hypergraphs. Extensive experiments have been conducted on real-life hypergraph databases to show both the effectiveness and efficiency of our proposed frameworks and algorithms.

CREDIBILISTIC ROBUST ONLINE FUZZY CLUSTERING IN DATA STREAM MINING TASKS

Context. The task of clustering-classification without a teacher of data arrays occupies an important place in the general problem of Data Mining, and for its solution there exists currently many approaches, methods and algorithms. There are quite a lot of situations where the real data to be clustered are corrupted with anomalous outliers or disturbances with non-Gaussian distributions. It is clear that “classical” methods of artificial intelligence (both batch and online) are ineffective in this situation. The goal of the paper is to develop a credibilistic robust online fuzzy clustering method that combines the advantages of credibilistic and robust approaches in fuzzy clustering tasks.
 Objective. The goal of the work is online credibilistic fuzzy clustering of distorted data, using of credibility theory in data stream mining.
 Method. The procedure of fuzzy clustering of data using credibilistic approach based on the use of both robust goal functions of a special type, insensitive to outliers and designed to work both in batch and its recurrent online version designed to solve Data Stream Mining problems when data are fed to processing sequentially in real time.
 Results. Analyzing the obtained results overall accuracy of clustering methods and algorithm, proposed method similar with result of credibilistic fuzzy clustering method, but has time superiority regardless of the number observations that fed on clustering process.
 Conclusions. The problem of fuzzy clustering of data streams contaminated by anomalous non-Gaussian distributions is considered. A recurrent credibilistic online algorithm based on the objective function of a special form is introduced, which suppresses these outliers by using the hyperbolic tangent function, which, in addition to neural networks, is used in robust estimation tasks. The proposed algorithm is quite simple in numerical implementation and is a generalization of some well-known online fuzzy clustering procedures intended for solving Data Stream Mining problems.