Displaying publications 1 - 20 of 142 in total

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  1. Environmental geochemistry of the abandoned Mamut Copper Mine (Sabah) Malaysia
    van der Ent A, Edraki M
    Environ Geochem Health, 2018 Feb;40(1):189-207.
    PMID: 27848090 DOI: 10.1007/s10653-016-9892-3
    The Mamut Copper Mine (MCM) located in Sabah (Malaysia) on Borneo Island was the only Cu-Au mine that operated in the country. During its operation (1975-1999), the mine produced 2.47 Mt of concentrate containing approximately 600,000 t of Cu, 45 t of Au and 294 t of Ag, and generated about 250 Mt of overburden and waste rocks and over 150 Mt of tailings, which were deposited at the 397 ha Lohan tailings storage facility, 15.8 km from the mine and 980 m lower in altitude. The MCM site presents challenges for environmental rehabilitation due to the presence of large volumes of sulphidic minerals wastes, the very high rainfall and the large volume of polluted mine pit water. This indicates that rehabilitation and treatment is costly, as for example, exceedingly large quantities of lime are needed for neutralisation of the acidic mine pit discharge. The MCM site has several unusual geochemical features on account of the concomitant occurrence of acid-forming sulphide porphyry rocks and alkaline serpentinite minerals, and unique biological features because of the very high plant diversity in its immediate surroundings. The site hence provides a valuable opportunity for researching natural acid neutralisation processes and mine rehabilitation in tropical areas. Today, the MCM site is surrounded by protected nature reserves (Kinabalu Park, a World Heritage Site, and Bukit Hampuan, a Class I Forest Reserve), and the environmental legacy prevents de-gazetting and inclusion in these protected area in the foreseeable future. This article presents a preliminary geochemical investigation of waste rocks, sediments, secondary precipitates, surface water chemistry and foliar elemental uptake in ferns, and discusses these results in light of their environmental significance for rehabilitation.
    Matched MeSH terms: Mining*
  2. Fulltext The removal of uranium from aqueous solution
    Zulfadli Ahmad, Saifuddin Normanbhay
    Journal of Nuclear and Related Technologies, 2012;2012(2):108-120.
    MyJurnal
    This paper reviews the literature on uranium contamination and the removal of uranium from wastewater stemming from mining activities and nuclear power generation. After reviewing the applications of uranium in power generation, military, industry and scientific, this review discusses uranium and rare earth elements in wastewaters and the toxicity of uranium on aquatic life and humans. Further, various methods of removal of heavy metal contaminants including uranium are reviewed with special focus on the adsorption process and carbon nanotubes as a superior adsorbent.
    Matched MeSH terms: Mining
  3. Fulltext A review of subsequence time series clustering
    Zolhavarieh S, Aghabozorgi S, Teh YW
    ScientificWorldJournal, 2014;2014:312521.
    PMID: 25140332 DOI: 10.1155/2014/312521
    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.
    Matched MeSH terms: Data Mining*
  4. Pollutant source, ecological and human health risks assessment of heavy metals in soils from coal mining areas in Xinjiang, China
    Zhang H, Zhang F, Song J, Tan ML, Kung HT, Johnson VC
    Environ Res, 2021 11;202:111702.
    PMID: 34284019 DOI: 10.1016/j.envres.2021.111702
    This study aims to analyze the pollution characteristics and sources of heavy metal elements for the first time in the Zhundong mining area in Xinjiang using the linear regression model. Additionaly, the health risks with their probability and infleuencing factors on different groups of people's were also evaluated using Monte Carlo (MC) simulation approach. The results shows that 89.28% of Hg was from coal combustion, 40.28% of Pb was from transportation, and 19.54% of As was from atmospheric dust. The main source of Cu and Cr was coal dust, Hg has the greatest impact on potential ecological risks. which accounted for 60.2% and 81.46% of the Cu and Cr content in soil, respectively. The all samples taken from Pb have been Extremely polluted (100%). 93.3% samples taken from As have been Extremely polluted. The overall potential ecological risk was moderate. Adults experienced higher non-carcinogenic risks of heavy metals from their diets than children. Interestingly, body weight was the main factor affecting the adult's health risks. This research provides more comprehensive information for better soil management, soil remediation, and soil pollution control in the Xinjiang mining areas.
    Matched MeSH terms: Coal Mining*
  5. Water Quality Modelling for River Activities Management: Example from a Low- and Middle-Income Country
    Zahidi I, Wilson G, Brown K, Hou FKK
    J Health Pollut, 2020 Dec;10(28):201207.
    PMID: 33324504 DOI: 10.5696/2156-9614-10.28.201207
    Background: Rivers are susceptible to pollution and water pollution is a growing problem in low- and middle-income countries (LMIC) with rapid development and minimal environmental protections. There are universal pollutant threshold values, but they are not directly linked to river activities such as sand mining and aquaculture. Water quality modelling can support assessments of river pollution and provide information on this important environmental issue.

    Objectives: The objective of the present study was to demonstrate water quality modelling methodology in reviewing existing policies for Malaysian river catchments based on an example case study.

    Methods: The MIKE 11 software developed by the Danish Hydraulic Institute was used to model the main pollutant point sources within the study area - sand mining and aquaculture. Water quality data were obtained for six river stations from 2000 to 2015. All sand mining and aquaculture locations and approximate production capacities were quantified by ground survey. Modelling of the sand washing effluents was undertaken with the advection-dispersion module due to the nature of the fine sediment. Modelling of the fates of aquaculture deposits required both advection-dispersion and Danish Hydraulic Institute ECO Lab modules to simulate the detailed interactions between water quality determinants.

    Results: According to the Malaysian standard, biochemical oxygen command (BOD) and ammonium (NH4) parameters fell under Class IV at most of the river reaches, while the dissolved oxygen (DO) parameter varied between Classes II to IV. Total suspended solids (TSS) fell within Classes IV to V along the mid river reaches of the catchment.

    Discussion: Comparison between corresponding constituents and locations showed that the water quality model reproduced the long-term duration exceedance for the main body of the curves. However, the water quality model underestimated the infrequent high concentration observations. A standard effluent disposal was proposed for the development of legislation and regulations by authorities in the district that could be replicated for other similar catchments.

    Conclusions: Modelling pollutants enables observation of trends over the years and the percentage of time a certain class is exceeded for each individual pollutant. The catchment did not meet Class II requirements and may not be able to reach Class I without extensive improvements in the quality and reducing the quantity of both point and non-point effluent sources within the catchment.

    Competing Interests: The authors declare no competing financial interests.

    Matched MeSH terms: Mining
  6. Fulltext In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology
    Yusuf N, Zakaria A, Omar MI, Shakaff AY, Masnan MJ, Kamarudin LM, et al.
    BMC Bioinformatics, 2015;16:158.
    PMID: 25971258 DOI: 10.1186/s12859-015-0601-5
    Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
    Matched MeSH terms: Data Mining
  7. Fulltext Modified multi-class classification using association rule mining
    Yuhanis Yusof, Mohammed Hayel Refai
    Pertanika Journal of Science & Technology, 2013;21(1):205-216.
    MyJurnal
    As the amount of document increases, automation of classification that aids the analysis and management of documents receive focal attention. Classification, based on association rules that are generated from a collection of documents, is a recent data mining approach that integrates association rule mining and classification. The existing approaches produces either high accuracy with large number of rules or a small number of association rules that generate low accuracy. This work presents an association rule mining that employs a new item production algorithm that generates a small number of rules and produces an acceptable accuracy rate. The proposed method is evaluated on UCI datasets and measured based on prediction accuracy and the number of generated association rules. Comparison is later made against an existing classifier, Multi-class Classification based on Association Rule (MCAR). From the undertaken experiments, it is learned that the proposed method produces similar accuracy rate as MCAR but yet uses lesser number of rules.
    Matched MeSH terms: Data Mining
  8. Vegetation restoration effects on soil carbon and nutrient concentrations and enzymatic activities in post-mining lands are mediated by mine type, climate, and former soil properties
    Yuan C, Wu F, Wu Q, Fornara DA, Heděnec P, Peng Y, et al.
    Sci Total Environ, 2023 Jun 25;879:163059.
    PMID: 36963687 DOI: 10.1016/j.scitotenv.2023.163059
    Vegetation restoration is a widely used, effective, and sustainable method to improve soil quality in post-mining lands. Here we aimed to assess global patterns and driving factors of potential vegetation restoration effects on soil carbon, nutrients, and enzymatic activities. We synthesized 4838 paired observations extracted from 175 publications to evaluate the effects that vegetation restoration might have on the concentrations of soil carbon, nitrogen, and phosphorus, as well as enzymatic activities. We found that (1) vegetation restoration had consistent positive effects on the concentrations of soil organic carbon, total nitrogen, available nitrogen, ammonia, nitrate, total phosphorus, and available phosphorus on average by 85.4, 70.3, 75.7, 54.6, 58.6, 34.7, and 60.4 %, respectively. Restoration also increased the activities of catalase, alkaline phosphatase, sucrase, and urease by 63.3, 104.8, 125.5, and 124.6 %, respectively; (2) restoration effects did not vary among different vegetation types (i.e., grass, tree, shrub and their combinations) or leaf type (broadleaved, coniferous, and mixed), but were affected by mine type; and (3) latitude, climate, vegetation species richness, restoration year, and initial soil properties are important moderator variables, but their effects varied among different soil variables. Our global scale study shows how vegetation restoration can improve soil quality in post-mining lands by increasing soil carbon, nutrients, and enzymatic activities. This information is crucial to better understand the role of vegetation cover in promoting the ecological restoration of degraded mining lands.
    Matched MeSH terms: Mining
  9. Mine land reclamation, mine land reuse, and vegetation cover change: An intriguing case study in Dartford, the United Kingdom
    Yu H, Zahidi I, Liang D
    Environ Res, 2023 May 15;225:115613.
    PMID: 36870554 DOI: 10.1016/j.envres.2023.115613
    Dartford, a town in England, heavily relied on industrial production, particularly mining, which caused significant environmental pollution and geological damage. However, in recent years, several companies have collaborated under the guidance of the local authorities to reclaim the abandoned mine land in Dartford and develop it into homes, known as the Ebbsfleet Garden City project. This project is highly innovative as it not only focuses on environmental management but also provides potential economic benefits, employment opportunities, builds a sustainable and interconnected community, fosters urban development and brings people closer together. This paper presents a fascinating case that employs satellite imagery, statistical data, and Fractional Vegetation Cover (FVC) calculations to analyse the re-vegetation progress of Dartford and the development of the Ebbsfleet Garden City project. The findings indicate that Dartford has successfully reclaimed and re-vegetated the mine land, maintaining a high vegetation cover level while the Ebbsfleet Garden City project has advanced. This suggests that Dartford is committed to environmental management and sustainable development while pursuing construction projects.
    Matched MeSH terms: Mining*
  10. The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data
    Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al.
    Nat Biotechnol, 2020 06;38(6):679-684.
    PMID: 32440006 DOI: 10.1038/s41587-020-0532-1
    Matched MeSH terms: Data Mining
  11. Fulltext Synthetic-type control charts for time-between-events monitoring
    Yen FY, Chong KM, Ha LM
    PLoS One, 2013;8(6):e65440.
    PMID: 23755231 DOI: 10.1371/journal.pone.0065440
    This paper proposes three synthetic-type control charts to monitor the mean time-between-events of a homogenous Poisson process. The first proposed chart combines an Erlang (cumulative time between events, Tr ) chart and a conforming run length (CRL) chart, denoted as Synth-Tr chart. The second proposed chart combines an exponential (or T) chart and a group conforming run length (GCRL) chart, denoted as GR-T chart. The third proposed chart combines an Erlang chart and a GCRL chart, denoted as GR-Tr chart. By using a Markov chain approach, the zero- and steady-state average number of observations to signal (ANOS) of the proposed charts are obtained, in order to evaluate the performance of the three charts. The optimal design of the proposed charts is shown in this paper. The proposed charts are superior to the existing T chart, Tr chart, and Synth-T chart. As compared to the EWMA-T chart, the GR-T chart performs better in detecting large shifts, in terms of the zero- and steady-state performances. The zero-state Synth-T4 and GR-Tr (r = 3 or 4) charts outperform the EWMA-T chart for all shifts, whereas the Synth-Tr (r = 2 or 3) and GR-T 2 charts perform better for moderate to large shifts. For the steady-state process, the Synth-Tr and GR-Tr charts are more efficient than the EWMA-T chart in detecting small to moderate shifts.
    Matched MeSH terms: Coal Mining
  12. Fulltext A novel approach for heart disease prediction using strength scores with significant predictors
    Yazdani A, Varathan KD, Chiam YK, Malik AW, Wan Ahmad WA
    BMC Med Inform Decis Mak, 2021 06 21;21(1):194.
    PMID: 34154576 DOI: 10.1186/s12911-021-01527-5
    BACKGROUND: Cardiovascular disease is the leading cause of death in many countries. Physicians often diagnose cardiovascular disease based on current clinical tests and previous experience of diagnosing patients with similar symptoms. Patients who suffer from heart disease require quick diagnosis, early treatment and constant observations. To address their needs, many data mining approaches have been used in the past in diagnosing and predicting heart diseases. Previous research was also focused on identifying the significant contributing features to heart disease prediction, however, less importance was given to identifying the strength of these features.

    METHOD: This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining.

    RESULTS: A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease.

    CONCLUSION: This study managed to provide a significant contribution in computing the strength scores with significant predictors in heart disease prediction. From the evaluation results, we obtained important rules and achieved highest confidence score by utilizing the computed strength scores of significant predictors on Weighted Associative Rule Mining in predicting heart disease.

    Matched MeSH terms: Data Mining
  13. Improved GART neural network model for pattern classification and rule extraction with application to power systems
    Yap KS, Lim CP, Au MT
    IEEE Trans Neural Netw, 2011 Dec;22(12):2310-23.
    PMID: 22067292 DOI: 10.1109/TNN.2011.2173502
    Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
    Matched MeSH terms: Data Mining/methods*
  14. Failure characteristic and fracture evolution law of overburden of thick coal in fully mechanized sub-level caving mining
    Xiaolei Wang, Qirong Qin, Cunhui Fan
    Sains Malaysiana, 2017;46:2041-2048.
    In mining process, the height of water flowing fractured zone is important significance to prevent mine of water and gas, in order to further research the failure characteristic of the overlying strata. Taking certain coal mine with 5.82 m mining height as the experimental face, by using the equipment which is sealed two ends by capsules in borehole, affused measurable water between the two capsules and borehole televiewer system, ground penetrating radar, microseismic monitoring system in underground coal mine, the height of water flowing fractured zone of fully-mechanized top caving are monitored, a numerical simulation experiment on the failure process was conducted, a similarity simulation experiment on the cracks evolution was conducted, at the same time, empirical formula of traditional was modified, The results showed that the height of caving and fractured zones were respectively 43.1 and 86.7 m in fully mechanized sub-level caving mining. The data difference of each test method of caving, fractured and water flowing fractured zones were respectively less than 4.5%, 7.1% and 9.0%. The degree of fracture development was low before mining, the number of fissures was obviously increased after mining, the degree of fracture development increased. The fractures cluster region mainly focuses near the coal wall. The fractures density distribution curves of overlying strata like sanke-shapes. The new and adapt to certain coal mine geological conditions empirical formula of water flowing fractured zone height is proposed.
    Matched MeSH terms: Mining
  15. Characterization of an autotrophic bioreactor microbial consortium degrading thiocyanate
    Watts MP, Spurr LP, Gan HM, Moreau JW
    Appl Microbiol Biotechnol, 2017 Jul;101(14):5889-5901.
    PMID: 28510801 DOI: 10.1007/s00253-017-8313-6
    Thiocyanate (SCN-) forms as a by-product of cyanidation during gold ore processing and can be degraded by a variety of microorganisms utilizing it as an energy, nitrogen, sulphur and/or carbon source. In complex consortia inhabiting bioreactor systems, a range of metabolisms are sustained by SCN- degradation; however, despite the addition or presence of labile carbon sources in most bioreactor designs to date, autotrophic bacteria have been found to dominate key metabolic functions. In this study, we cultured an autotrophic SCN--degrading consortium directly from gold mine tailings. In a batch-mode bioreactor experiment, this consortium degraded 22 mM SCN-, accumulating ammonium (NH4+) and sulphate (SO42-) as the major end products. The consortium consisted of a diverse microbial community comprised of chemolithoautotrophic members, and despite the absence of an added organic carbon substrate, a significant population of heterotrophic bacteria. The role of eukaryotes in bioreactor systems is often poorly understood; however, we found their 18S rRNA genes to be most closely related to sequences from bacterivorous Amoebozoa. Through combined chemical and phylogenetic analyses, we were able to infer roles for key microbial consortium members during SCN- biodegradation. This study provides a basis for understanding the behaviour of a SCN- degrading bioreactor under autotrophic conditions, an anticipated approach to remediating SCN- at contemporary gold mines.
    Matched MeSH terms: Mining
  16. In Situ Stimulation of Thiocyanate Biodegradation through Phosphate Amendment in Gold Mine Tailings Water
    Watts MP, Gan HM, Peng LY, Lê Cao KA, Moreau JW
    Environ Sci Technol, 2017 Nov 21;51(22):13353-13362.
    PMID: 29064247 DOI: 10.1021/acs.est.7b04152
    Thiocyanate (SCN-) is a contaminant requiring remediation in gold mine tailings and wastewaters globally. Seepage of SCN--contaminated waters into aquifers can occur from unlined or structurally compromised mine tailings storage facilities. A wide variety of microorganisms are known to be capable of biodegrading SCN-; however, little is known regarding the potential of native microbes for in situ SCN- biodegradation, a remediation option that is less costly than engineered approaches. Here we experimentally characterize the principal biogeochemical barrier to SCN- biodegradation for an autotrophic microbial consortium enriched from mine tailings, to arrive at an environmentally realistic assessment of in situ SCN- biodegradation potential. Upon amendment with phosphate, the consortium completely degraded up to ∼10 mM SCN- to ammonium and sulfate, with some evidence of nitrification of the ammonium to nitrate. Although similarly enriched in known SCN--degrading strains of thiobacilli, this consortium differed in its source (mine tailings) and metabolism (autotrophy) from those of previous studies. Our results provide a proof of concept that phosphate limitation may be the principal barrier to in situ SCN- biodegradation in mine tailing waters and also yield new insights into the microbial ecology of in situ SCN- bioremediation involving autotrophic sulfur-oxidizing bacteria.
    Matched MeSH terms: Mining
  17. Mapping the cumulative impacts of long-term mining disturbance and progressive rehabilitation on ecosystem services
    Wang Z, Lechner AM, Yang Y, Baumgartl T, Wu J
    Sci Total Environ, 2020 May 15;717:137214.
    PMID: 32062237 DOI: 10.1016/j.scitotenv.2020.137214
    Open-cut coal mining can seriously disturb and reshape natural landscapes which results in a range of impacts on local ecosystems and the services they provide. To address the negative impacts of disturbance, progressive rehabilitation is commonly advocated. However, there is little research focusing on how these impacts affect ecosystem services within mine sites and changes over time. The aim of this study was to assess the cumulative impacts of mining disturbance and rehabilitation on ecosystem services through mapping and quantifying changes at multiple spatial and temporal scales. Four ecosystem services including carbon sequestration, air quality regulation, soil conservation and water yield were assessed in 1989, 1997, 2005 and 2013. Disturbance and rehabilitation was mapped using LandTrendr algorithm with Landsat. We mapped spatial patterns and pixel values for each ecosystem service with corresponding model and the landscape changes were analyzed with landscape metrics. In addition, we assessed synergies and trade-offs using Spearman's correlation coefficient for different landscape classes and scales. The results showed that carbon sequestration, air quality regulation and water yield services were both positively and negatively affected by vegetation cover changes due to mined land disturbance and rehabilitation, while soil conservation service were mainly influenced by topographic changes. There were strong interactions between carbon sequestration, air quality regulation and water yield, which were steady among different spatial scales and landscape types. Soil conservation correlations were weak and changed substantially due to differences of spatial scales and landscape types. Although there are limitations associated with data accessibility, this study provides a new research method for mapping impacts of mining on ecosystem services, which offer spatially explicit information for decision-makers and environmental regulators to carry out feasible policies, balancing mining development with ecosystem services provision.
    Matched MeSH terms: Coal Mining
  18. Science, politics, and radiation
    Wagner HN
    JAMA, 1988 Aug 5;260(5):697-8.
    PMID: 3392799
    Matched MeSH terms: Mining*
  19. Fulltext BioHackathon 2015: Semantics of data for life sciences and reproducible research
    Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, et al.
    F1000Res, 2020;9:136.
    PMID: 32308977 DOI: 10.12688/f1000research.18236.1
    We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.
    Matched MeSH terms: Data Mining
  20. Fulltext An Empirical Analysis of Rough Set Categorical Clustering Techniques
    Uddin J, Ghazali R, Deris MM
    PLoS One, 2017;12(1):e0164803.
    PMID: 28068344 DOI: 10.1371/journal.pone.0164803
    Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.
    Matched MeSH terms: Data Mining
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