Miss i mining

On May 28, Germany apologized. The German apology is a commendable step and important precedent. Millions of carats in diamonds have been exported from Namibia since These same sparkling stones have a dirty history tied to German colonial rule. The debt Germany owes is construed as limited to the recognized period of genocide—even though the real money that Germans made and controlled in Namibia came after , and the process of making that money implicated many parts of the world in a deadly, brutal colonial process. For much of modern history diamonds were rare, at least outside of India and Brazil.

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Miss i mining

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Content:

• Iamgold workers missing in Burkina Faso 'safely located'
• What is Data Mining?
• Nagorno-Karabakh conflict aftermath: Winter deepens agony of families of missing people
• Dozens feared missing in Myanmar mine landslide
• I Miss Calling You Mine Quotes & Sayings
• The missing link in mining-company productivity: People
• Miss Tracy
• I miss going on mining runs for charms : (

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Iamgold workers missing in Burkina Faso 'safely located'

Many existing, industrial and research data sets contain Missing Values. They are introduced due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Hence, it is usual to find missing data in most of the information sources used. The detection of incomplete data is easy in most cases, looking for Null values in a data set.

However, this is not always true, since Missing Values MVs can appear with the form of outliers or even wrong data i.

Missing values make it difficult for analysts to perform data analysis. Three types of problems are usually associated with missing values J. Barnard, X. Methods Med. Farhangfar et al A. Farhangfar, L. Kurgan, J. Impact of imputation of missing values on classification error for discrete data. Pattern Recognition 41 , doi: The simplest way of dealing with missing values is to discard the examples that contain the missing values.

However, this method is practical only when the data contain relatively small number of examples with missing values and when analysis of the complete examples will not lead to serious bias during the inference.

Another approach is to convert the missing values into a new value encode them into a new numerical value , but such simplistic method was shown to lead to serious inference problems. On the other hand, if a significant number of examples contain missing values for relatively small number of attributes, it may be beneficial to perform imputation filling-in of the missing values.

We will center our attention on the use of imputation methods. A fundamental advantage of this approach is that the missing data treatment is independent of the learning algorithm used. For this reason, the user can select the most appropriate method for each situation he faces. There is a wide family of imputation methods, from mean imputation to those which analyze the relationships between attributes. In the other hand, it is important to categorize the mechanisms which lead to the introduction of MVs R.

Little and D. Statistical analysis with Missing Data. John Wiley and Sons, New York, Such mechanism will determine which imputation method could be applied, if any. As Little and Rubin stated, there exist three different mechanism of missing data induction. In case of the MCAR mode, the assumption is that the distributions of missing and complete data are the same, while for MAR mode they are different, and the missing data can be predicted by using the complete data R.

These two mechanisms are assumed by the most imputation methods so far. There exist many imputation methods published, but their use in the Data Mining field is limited. A very recent study only mention 4 big MVs studies in this field A. Deeper searchs cand find some extra studies with less magnitude. However, methods from other related fields can be adapted to be used as imputation methods. The imputation methods we have considered are briefly described next:.

This Section presents a collection of the complete technical description of the Missing Values methods listed in the previous section. We have maintained the same notation and formulation than the original reference papers where the imputation methos were originally presented. Imputation of Missing Values. Method's Description. The literature on imputation methods in data mining employs well-known machine learning methods for their studies, in which the authors show the convenience of imputing the MVs for the mentioned algorithms, particularly for classification.

The vast majority of MVs studies in classification usually analyze and compare one imputation method against a few others under controlled amounts ofMVs and induce them artificially with known mechanisms and probability distributions Acuna E, Rodriguez C Classification, clustering and data mining applications.

Springer, Berlin, pp —, Batista G. Appl Artif Intell 17 5 —, Farhangfar A. Pattern Recognit 41 12 —, Hruschka E. J Intell Inf Syst 29 3 — In this context, more information needs to be given in order to establish the best imputation strategy for each classifier.

It is reasonable to expect that similar classifiers will have similar response to the same imputation data. We will establish three groups of classifiers to categorize them, and we will examine the best imputation strategies for each group.

The former groups are as follows:. By means of a large number of data sets see next section , the best imputation strategy can be established for each classifier.

We consider the use of the non-parametric pairwise statistical test formerly known as the Wilcoxon Signed Rank test to do so. Using this statistical test, each imputation method is compared against the others for each classifier. A final rank will establish the best imputation method for such classifier. The document with the final Wilcoxon's test results follows:.

Tables with the Wilcoxon Signed Rank test results summarized for each classification method. Aggregating the best imputation method information obtained for each classifier using the aforementioned classifier categories, a final ranking of the best imputation strategy for each category can be stated.

The following tables show the best imputation methods for each category and it can be seen that they differ from one category to another. From the results shown, the use of the FKMI and EC imputation methods is the best choice under general assumptions but showing little advantage with respect to the rest of imputation methods analyzed.

It is also interesting to relate the influence of the imputation methods to the performance obtained by the classification method based on the information contained in the data set. They are described as follows:. For each instance of interest, the method looks for the K nearest neighbors using the Euclidean distance and uses the class labels of such neighbors in order to classify the considered instance.

If the instance is not correctly classified, then the variable noise is increased by one unit. Therefore, the final noise ratio will be.

Mutual information MI is considered to be a good indicator of relevance between two random variables [12]. In the next step, we compute the ratio between each one of these values, considering the imputation of the data set with one imputation method in respect of the not imputed data set.

The average of these ratios will show us if the imputation of the data set produces a gain in information:. Averaging the rankings of Wilson's noise and Mutual Information for each imputation across all the data sets considered see next section , we can establish the imputation method which introduces less noise and the imputation method which produces a greatest gain of information.

With the analysis performed, we have quantified the noise induced by the imputation methods and how the relationship between each input attribute and the class is maintained. We have shown that the CMC and EC methods show good behavior for these two measures, and they are two methods that provide good results for an important range of learning methods, as we have previously analyzed. In short, these two approaches introduce less noise and maintain the mutual information better. Below you can find all the Missing values data sets available.

For each data set, it is shown its name and its number of instances, attributes, classes number of possible values of the output variable and the percentages of values with missing values and examples with missing values.

Next, it provides a set of classification data set with missing values induced. Test partitions remains unchanged. Also, you can download all datasets by clicking ZIP file.

The presence of Missing Values in a data set can affect the performance of a classifier constructed using that data set as a training sample. Several methods have been proposed to treat missing data and the one used more frequently is the imputation of the Missing Values of an instance.

In this paper, we analyze the improvement of performance on Radial Basis Function Networks by means of the use of several imputation methods in the classification task with missing values. The study has been conducted using data sets with real Missing Values, and data sets with artificial Missing Values. It allows us to overcome the negative impact of the presence of Missing Values to a certain degree. Data sets used: ZIP file. Results obtained: ZIP file. On the choice of the best imputation methods for missing values considering three groups of classification methods.

In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on a classification task with twenty-three classification methods and fourteen different imputation approaches to missing values treatment that are presented and analyzed.

The analysis involves a group-based approach, in which we distinguish between three different categories of classificationmethods. Each category behaves differently, and the evidence obtained shows that the use of determined missing values imputation methods could improve the accuracy obtained for these methods. In this study, the convenience of using imputation methods for preprocessing data sets with missing values is stated.

The analysis suggests that the use of particular imputation methods conditioned to the groups is required. XLS file. CSV file. We have performed a bibliography compilation of journal papers on Missing Values in Data Mining. It is maintained by J. If you would like to include or correct any of the references on this page, please contact the maintainer in his e-mail address: julianlm decsai.

Technical description report This Section presents a collection of the complete technical description of the Missing Values methods listed in the previous section. Open source implementations of these methods can be found in KEEL Software On the suitability of imputation methods for different learning approaches The literature on imputation methods in data mining employs well-known machine learning methods for their studies, in which the authors show the convenience of imputing the MVs for the mentioned algorithms, particularly for classification.

J Intell Inf Syst 29 3 — In this context, more information needs to be given in order to establish the best imputation strategy for each classifier. The former groups are as follows: The first group consists of the rule induction learning category.

What is Data Mining?

Lincoln Gold Mining Inc. Shing Lee, a director of Lincoln since In addition to his tenure with the Company, Mr. His commitment, guidance and support will be greatly missed. He was a well-liked and much respected colleague and we will greatly miss his friendship, guidance and unwavering support.

Nagorno-Karabakh conflict aftermath: Winter deepens agony of families of missing people

Amadeus use cookies on this website. They help us to know a little bit about you and how you use our website, which improves the browsing experience and marketing - both for you and for others. They are stored locally on your computer or mobile device. To accept cookies, continue browsing as normal. Or, go to the privacy policy for more information. Data preparation is a large part of the data mining process. The two nodes available within SAS Enterprise Miner for the purpose of replacing missing values include:. The Replacement node is used specifically for the replacement of missing class and interval variables within the data set based on specific values. However, the Impute node is used to replace missing class and interval variables with the use of a number of available options including; the mean of that variable, the median value or a tree-based imputation method for identifying a replacement value. Each node can be dragged on to a SAS Enterprise Miner diagram and can be joined using an arrow to direct the flow of the data through the system:.

Dozens feared missing in Myanmar mine landslide

A simple synthetic event log running-example. Note that several real event logs have been made available, over the past few years. You can find them here. The code fragment uses the standard importer iterparse, described in a later paragraph. Event logs are stored as an extension of the Python list data structure.

I Miss Calling You Mine Quotes & Sayings

A common problem of very sensitive but technically complex high-throughput techniques that detect the concentation of hundreds to thousand of proteins, RNA transcripts or genomic features, is that it is not possible to know if a feature might not be detected because of a technical problem value missing at random or because the feature was truly absent below the limit of detection, missing not at random. Imputation methods detect associations between features in sets of samples and then evaluate if a distribution of detected features is likely to imply that a missing feature should or should not have been detected. Application of imputation methods is thus expected to complete the data and increase its quality. The risk is the introduction of unexpected biases. Proximity extension assays PEA use paired antibodies that are oligonucleotide-labeled.

The missing link in mining-company productivity: People

Over 80 people are reported to be missing after a landslide hit a jade mining site near the Thayar Gone village in Hpakant Township in north Myanmar in the early hours of December Rescue teams of the fire department of Kachin State have recovered three bodies so far. Most of those missing are reportedly jade mine workers and scavengers at the waste site who were swept into the lake and buried under the debris of the landslide. As reported by Reuters , the Kachin Network Development Foundation, a local civil society group involved in the rescue operation, has estimated the number of missing at around Deadly landslides regularly occur at Hpakant due to rampant overmining of the hills. A similar accident had occurred in July last year whe n a mountain of mining waste collapsed into a lake leading to the death of at least people at the site.

Miss Tracy

Many existing, industrial and research data sets contain Missing Values. They are introduced due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. Hence, it is usual to find missing data in most of the information sources used. The detection of incomplete data is easy in most cases, looking for Null values in a data set.

I miss going on mining runs for charms : (

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At least one person has died and dozens are still missing after a landslide at a jade mine in northern Myanmar, an emergency official has said. About rescue workers are involved in the search, some in boats trying to recover bodies from a lake, he added. The Myanmar Fire Services Department said on Facebook that fire stations in Hpakant and neighbouring city, Lone Khin as well as the Lone Khin Regional fire department were involved in the rescue operations. Dozens of miners in Myanmar die each year working in dangerous conditions in jade quarries, an opaque and poorly regulated industry. Landslides are common in this poor and inaccessible region, which looks like a moonscape because it has been so badly altered by large mining companies, with little regard for the environment.

At least 70 people are feared missing after a landslide occurred at a jade mining site in northern Myanmar. Rescue operations are currently underway, with most of the victims are believed to be illegal jade miners. Myanmar is the world's biggest source of jade but its mines have seen numerous accidents over the years. Video: Malaysians evacuated in flood-hit area Reuters. Jade mining is banned in Hpakant, but locals often defy regulations, driven by lack of employment and impoverished conditions that have worsened from the Covid pandemic. Several days ago, at least 10 unskilled miners went missing in a landslide at a jade block in Hpakant.

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