Subject – Data Mining. Q)Discuss the advantages and disadvantages of using sampling to reduce the number of data objects that need to be displayed. Would simple random sampling (without replacement) be a good approach to sampling? Why or why not?( 300 words ) Subject – info security and risk management. Q) The Department of Health and Human Services (the agency responsible for managing HIPAA compliance among healthcare providers) lists recent breaches at  – think of it as their “Wall of Shame.” Find an article online that discusses a breach or violation of a regulation, such as HIPAA, or of a standard such as PCI-DSS, GLBA, or FERPA. You can also look at Federal Agencies and discuss those that have not had sufficient controls in place (think of the breach that the Office of Personnel Management had). Summarize the article in your own words and address the controls that the organization should have had in place, but didn’t, that facilitated the breach. What were the ramifications to the organization and the individuals involved?(300 words Apa format)

Advantages and Disadvantages of Using Sampling to Reduce Data Objects

Sampling is a commonly used technique in data mining to reduce the number of data objects that need to be displayed. By selecting a subset of the data, sampling allows analysts to work with a smaller, more manageable dataset. However, there are both advantages and disadvantages to using sampling in data mining.

One of the main advantages of sampling is that it saves computational resources and speeds up the analysis process. When dealing with large datasets, it may not be feasible to analyze the entire dataset due to time and resource constraints. By selecting a representative sample, analysts can obtain useful insights and make inferences about the entire dataset without having to process the entire dataset.

Another advantage of sampling is that it can help reduce bias in data analysis. In some cases, the data may have inherent biases due to various factors such as data collection methods or data quality issues. By carefully selecting a sample, analysts can help mitigate potential biases and obtain more accurate results.

Despite these advantages, there are also drawbacks to using sampling in data mining. One major disadvantage is the potential loss of information. By selecting only a subset of the data, analysts may miss out on crucial details that are present in the full dataset. This can lead to incomplete or biased analysis results, potentially leading to inaccurate conclusions.

Another disadvantage is the increased risk of sampling error. Sampling error occurs when the characteristics of the sample differ from the characteristics of the full dataset. This error can lead to incorrect inferences and generalizations about the population from which the sample was drawn. Proper sampling techniques, such as randomization and stratification, can help minimize sampling error, but it cannot be completely eliminated.

Now, considering the specific approach of simple random sampling without replacement, it may not always be the best sampling method in all scenarios. Simple random sampling involves randomly selecting data objects from the population without replacement, ensuring that each data object has an equal chance of being selected. While this approach has the advantage of simplicity and ease of implementation, it may not be appropriate in situations where there are specific requirements or constraints.

For example, in cases where the dataset has a natural ordering or structure, simple random sampling may not capture this structure effectively. In such cases, other sampling methods such as stratified sampling or systematic sampling that take into account the underlying structure can yield more representative results. Additionally, simple random sampling may not be suitable when the dataset contains outliers or rare events that need to be given more emphasis.

In conclusion, sampling is a valuable technique in data mining that has its advantages and disadvantages. While sampling can save computational resources, reduce bias, and speed up analysis, it also comes with the risk of information loss and sampling error. When selecting a sampling method, factors such as data characteristics, research objectives, and constraints should be carefully considered to ensure the most appropriate approach is employed.

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