The topic of big data is as complex as any other topic out there. One main focus of the healthcare industry is Defining Health Care Data Mining. The data in the healthcare industry is evergrowing and as we try to grow this big data, it is important that we also learn how to mine it properly. Data mining can help organizations and scientists to find and select the most important and relevant information.
This information can be used to create models that enable predictions on how patients or patient groups might behave so the healthcare organization can anticipate it. The more data that is mined, the better the models will become that you can create using the data mining techniques. Just like in statistics, the larger the sample size of a population, the more the result will reflect the characteristics of the population.
Different Data Minning Techniques
Regression analysis can be used in more than one way. In data mining, regression analysis is used to define the dependency between two or more variables. It tries to study and identify the causal effect of one variable to another. In regression analysis, you have to identify the dependent variable or variables and also the independent variables. The simplest regression analysis is the linear regression analysis and its formula is Y=a+bX (The value of b is calculated first, then the value of a is obtained using the value obtained for b).
A regression analysis can show that one variable is dependent on another but not vice-versa. If both variables depend on each other, then that is a correlation analysis. In a healthcare data mining situation, a regression analysis can be used to determine the satisfaction of a patient.
In clustering analysis, data sets that are similar to each other and have similar characteristics are identified and then analyzed in a cluster. The data sets that have the same characteristics or variables are placed in a cluster and this helps improve target algorithms. So, for example, a healthcare organization can place certain patients in clusters and monitor them as a group. That way treatment can be targeted with specifics. With continuous clustering, many different clusters can be created. These clusters make processing large tons of data easier and quicker.
Cluster detection is another type of pattern recognition that is particularly useful is recognizing distinct clusters or sub-categories within a dataset. Machine learning algorithms can detect all of the different subgroups within a dataset that differ significantly from each other. So in an e-commerce company, for example, the purchasing behavior of different types of customers can be grouped together into subsets. So if a person purchases hunting equipment repeatedly or even twice, they are grouped into the hunting subset. Same goes for fishermen, gardeners etc.
Outlier or detection is used to detect discrepancies or errors in a data set. During data mining, there is bound to be data sets or results that vary from the group, however, the datasets that are classified as outliers and anomalies, are data sets that vary so far off from the group, that they can’t be used, mined, or interpreted in any way that is useful.
When these datasets are examined, they can provide insights into the problems that exist within the data or potential problems that could. Anomalies are also called outliers, exceptions, surprises or contaminants and they often provide critical and actionable information that can be used during data mining. In the financial industry, outliers and anomalies can provide insights into fraud risks or changes in trends. When an outlier occurs, usually a data analyst should study it further instead of ignoring it.
When a dataset is really large it is expected that there should be a few outliers. However, when there are too many outliers within a dataset, it means that the data is bad data. Whatever the case, additional research on outliers and anomalies is always advised. When it comes to identifying outliers, they are not always visible, but with the help of tools like statistics, they can be identified.
Association learning is a type of machine learning data mining process that learns from previous actions and uses to predict future actions or preferences. It is used a lot in e-commerce companies. Have you ever been shopping online and you get recommendations on similar products or products that “you might like”.
That is machine learning associate learning at work. It is also used by companies like Netflix, Hulu, HBO Go and Amazon Video. On websites like Netflix, you get movie recommendations based on movies you had seen previously on the website. All the movies you get recommended, have similar characteristics with that subset. Companies like Spotify and Pandora also use this kind of machine learning techniques for their music recommendations and also for when the compile playlists or radio stations