What is Strip Mining? Surface mining, counting strip mining, mountaintop removal mining and open-pit mining, is a wide classification of mining where soil and rock covering the mineral source (the 'overburden') are gotten rid of, unlike underground mining (or deep mining) where the overlying rock is kept in place, and the mineral is taken out through shafts or tunnels.

Companies who use data mining techniques must act responsibly by being aware of the ethical issues that are surrounding their particular application; they must also consider the wisdom in what they are doing. For example, data mining sometimes can be used to discriminate people, especially regarding racial, sexual and religious orientations.

Using these data mining techniques efforts do not offer sufficient protection for web users' on web data creates another level of decision-making privacy. in which companies can use large amounts of detailed E THICAL ISSUES IN WEB DATA MINING 135 3rd Argument overall control on web content (and structure) data, and there are no rules (except ...

perspective of highwall stability issues. Eleven mines were visited to obtain data on their mining practice or design. Commodities included coal, sandstone, and limestone. Based on the visits, five case studies were developed to represent typical mining methods and effective ground control practices used in eastern surface mines. Benching was ...

Advanced Methods. Text Classification. Text classification is the process of assigning categories (tags) to unstructured text data. This essential task of Natural Language Processing (NLP) makes it easy to organize and structure complex text, turning it into meaningful data.. Thanks to text classification, businesses can analyze all sorts of information, from emails to support tickets, and ...

Surface Mining Methods and Equipment - EOLSS 1.2. Open Pit vs. Underground Mining Methods While most mineral deposits can be mined by either surface or underground methods, some minerals can only be recovered in a hybrid manner, usually by initial extracted by open pit mining, followed by underground methods. Here, a brief comparison is made

This Tutorial on Data Mining Process Covers Data Mining Models, Steps and Challenges Involved in the Data Extraction Process: Data Mining Techniques were explained in detail in our previous tutorial in this Complete Data Mining Training for All.Data Mining is a promising field in the world of science and technology.

Mining is the extraction of valuable minerals or other geological materials from the Earth, usually from an ore body, lode, vein, seam, reef, or placer deposit.These deposits form a mineralized commodity that is of economic interest to the miner. Ores recovered by mining include metals, coal, oil shale, gemstones, limestone, chalk, dimension stone, rock salt, potash, gravel, and clay.

Social Media Data Mining Methods. Applying data mining techniques to social media is relatively new as compared to other fields of research related to social network analytics. When we acknowledge the research in social media network analysis dates back to the 1930s. The application that uses data mining techniques developed by industry and ...

Mining is the extraction of minerals and other geological materials of economic value from deposits on the Earth. Mining adversely affects the environment by inducing loss of biodiversity, soil erosion, and contamination of surface water, groundwater, and soil. Mining can also trigger the formation of …

The paper reviews three major ethical issues that arise in data mining, particularly data mining involving one or more federal statistical data sets, in terms of the ASA's Ethical Guidelines. They are the suitability and validity of methods used, privacy and confidentiality, and the objectives of the data mining effort.

There are four main mining methods: underground, open surface (pit), placer, and in-situ mining. Underground mines are more expensive and are often used to reach deeper deposits. Surface mines are typically used for more shallow and less valuable deposits. Placer mining is used to sift out valuable metals from sediments in river channels, beach sands, or other environments.

Important issues that impact data mining methods are described in the context of the preterm case study. Carbonell identified four major machine learning paradigms as (1) inductive learning, (2) analytic learning, (3) genetic algorithms, and (4) connectionist learning methods. He also provided a definition of machine learning.

Data mining is a method of extracting data from multiple sources and organizing it to derive valuable insights. Read on to discover the wide-ranging data mining applications that are changing the industry as we know it!. Modern-day companies cannot live in a data lacuna.

This paper presents the literature review about the Big data Mining and the issues and challenges with emphasis on the distinguished features of Big Data. It also discusses some methods to deal ...

Often the data counts are so huge that several issues are faced when organizing them into a structured form. Challenges with manpower, time consumption, and even financial outputs arise with such situations. A similar issue arises when an enormous quantity of output from several diverse data mining methods is being collected.

Data Mining Techniques. Broadly speaking, there are seven main Data Mining techniques. 1. Statistics. It is a branch of mathematics which relates to the collection and description of data. A statistical technique is not considered as a Data Mining technique by many analysts. However, it helps to discover the patterns and build predictive models.

Open pit mining Mining Methods 5 Open pit mines are used to exploit low grade, shallow ore bodies. However, some pits are quite deep – about 1 km. The mining rate is greater than 20,000 tonnes per day (tpd) but is usually much greater. Some pits operate at a rate of more than 100,000 tpd

During the text mining process, important difficulties and obstacles such as domain knowledge integration, variable concept granularity, multilingual text refining, and natural language processing ambiguity occur. Synonyms and antonyms are all used in texts, which causes issues for text mining techniques that take both into account.

Underground mining Underground mining refers to a group of techniques used for the extraction of valuable minerals or other geological materials from the earth. 17. 1.Drift mining is a method of accessing valuable geological material, such as coal, by cutting into the side of the earth, rather than tunneling straight downwards. 18.

Major Issues In Data Analysis. Data Mining has a number of issues related to it as mentioned below: Mining Methodology. As there are diverse applications, new mining tasks continue to emerge. These tasks can use the same database in different ways and require the development of new data mining techniques.

It involves understanding the issues regarding different factors regarding mining techniques. Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web. Handling noise and incomplete data : data cleaning and data …

J ComputSci Issues 7(5) ... accuracy of the models evolve over time and especially the end-users who have a adequate level of knowledge from data mining benefit from the method. Moreover, the ...

Text Mining: Techniques, Applications and Issues. Rapid progress in digital data acquisition tech-niques have led to huge volume of data. More than 80 percent of today's data is composed of unstructured or semi-structured data. The discovery of appropriate patterns and trends to analyze the text documents from massive volume of data is a big ...

Web mining is the application of data mining techniques to discover patterns from the World Wide Web.It uses automated methods to extract both structured and unstructured data from web pages, server logs and link structures. There are three main sub-categories of web mining.

Surface mining methods can be broadly classified as open-pit mining, which includes quarrying, strip mining, contour mining, dredging, and hydraulic mining. Topography and the physical characteristics of the deposit strongly influence the choice of method.

Text Mining: Techniques, Applications and Issues. Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.071153. Article Published in International Journal of Advanced Computer Science and Applications (IJACSA), Volume 7 Issue 11, 2016. Abstract: Rapid progress in digital data acquisition tech-niques have led to huge volume of data.

c. Security Issues. As huge data is being collected in data mining systems, some of this data which is very critical might be hacked by hackers as happened with many big companies like Ford Motors, Sony etc. d. Additional irrelevant information Gathered. The main functions of the systems create a relevant space for beneficial information.

Mining is an inherently invasive process that can cause damage to a landscape in an area much larger than the mining site itself. The effects of this damage can continue years after a mine has shut down, including the addition to greenhouse gasses, death of flora and fauna, and erosion of land and habitat.

Mining techniques. Surface mining. Surface mining is done by removing (stripping) surface vegetation, dirt, and, if necessary, layers of bedrock in order to reach buried ore deposits. Techniques of surface mining include: open-pit mining, which is the recovery of materials from an open pit in the ground, quarrying, identical to open-pit mining ...

Moreover, the issues that arise during text mining process are identified. This paper is organized in different sections. Previous work is discussed in section II. In section III, different techniques of text mining are explained. Section IV presents the application areas of …

Data Mining Techniques. 1.Classification: This analysis is used to retrieve important and relevant information about data, and metadata. This data mining method helps to classify data in different classes. 2. Clustering: Clustering analysis is a data …