Data mining has become pivotal to realizing patterns and relationships between data items. The data and data classification approaches play a critical role in learning the value of data mining. The data is obtained from multiple sources and thus explains different patterns and relationships as required in data mining (Xu et al., 2014). Classifying data helps ensure that the data mining operations progress under the appropriate classes of data, thus enabling the data scientists to conveniently come up with the patterns, trends, and correlations in that data that otherwise could not be observed with unclassified data. Also, classifying data provides data scientists with various options for uncovering valuable insights during data mining. For example, classifying data helps identify the items that suit a specific technique for data mining that will help realize the most accurate and valuable results (Xu et al., 2014).
Association in data mining.
The association is the analytical approach in data mining that helps discover the relationship between large data volumes. Essentially, the association helps reveal relationships in items such as commodities that can be purchased together. The various areas where the concept of association as used in data mining can be conveniently used include; customer segmentation and market basket analysis, among others. Association identifies correlation in data which sets the foundation for discovering patterns, predicting the future, and discovering new opportunities.
Association rule concepts.
Tan, Steinbach & Kumar (2016) identify the Apriori algorithm as an association rule from the text. In this case, the Apriori algorithm mines the frequent item sets and association rules. One of the critical concepts of the Apriori algorithm is support which involves the measurement of the frequency exhibited in a particular dataset. In this case, a minimum threshold must be set before data analysis kicks off. Second, the Apriori algorithm involves confidence as a critical concept explaining the strength measure posited by the underlying association rule. To determine the confidence in the apriori algorithm, we need to establish the ratio of the frequency of occurrence of consequent item sets to that of an antecedent itemset. Another concept is the lift which involves the measure of advancement when it comes to predicting the accuracy of the association rule. The Apriori algorithm involves the double scan pruning that reduces the search space and the number of itemsets involved. In the association rule, the candidate generation concept involves the technique applied in generating the candidate item sets from the given dataset.
Cluster analysis.
Cluster analysis develops meaningful and valuable groups of data in which the natural structure of data is captured. In the case of data mining, cluster analysis serves starting point for other data mining operations that are meant to develop patterns and relationships from the data clusters created. Cluster analysis provides for detecting outliers in data, comprehension of data distributions, and identification of customer segments. The concept of unsupervised learning is fundamental in cluster analysis. Unsupervised learning implies that there is no need for prior knowledge regarding the given data when it comes to cluster analysis. Cluster analysis provides the similarity measure between data points through measurements such as the Euclidean distance (Tan, Steinbach & Kumar, 2016).
The concept of k-means clustering emerges as a primary approach to cluster analysis. Technically, this kind of cluster analysis performs by randomly identifying the cluster centers and assigning data points to the closest cluster center. Towards solving the practical, cluster analysis comprises multiple applications. Clustering for utility concept offers specific abstraction that is in line with the specific data object, which can be used as the foundation for additional data analysis or processing techniques. Also, there is clustering for understanding which contributes to how people analyze and describe the world by offering meaningful groups of data objects.
Anomaly.
In data mining, anomaly refers to the data points that tend to go against the norm expressed in the overall dataset and thus become so much unique from other data points. In this case, analysis becomes challenging, thus calling for something to be done. Therefore, the analytics tools are thus deployed to explore the patterns and establish the data points that have departed from the original pattern. The significance of the anomalies in different contexts included; the detection of intrusion detection, medical diagnostics, detection of fraud, and quality control in manufacturing, among others (Rousseeuw & Hubert, 2018).
Avoiding anomalies would best be achieved by boosting the represented data set entities. In this case, it is required that all the causes of the anomalies are first understood. Therefore, sharpening the entities would involve splitting a single dataset into two data sets. Also, one can avoid anomalies by normalizing all the data values to enable the algorithm to offer only the correct predictions, especially in the case of continuous values (Rousseeuw & Hubert, 2018).
False discoveries.
Various methods be used to avoid false discoveries. In this case, the method works based on controlling the significance threshold applied to determine statistical significance. One of these methods involves modeling the null and alternative distributions through resampling and generating synthetic data. Second, false discoveries can be avoided through a statistical testing method involving hypothesis testing and significant testing. The approach of anomaly detection can also be used to avoid false discoveries. Under classification, the statistical testing method would also be used to avoid false discoveries (Tan, Steinbach & Kumar, 2016).
References
Tan, P. N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education India.
Rousseeuw, P. J., & Hubert, M. (2018). Anomaly detection by robust statistics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), e1236. https://doi.org/10.1002/widm.1236
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data: privacy and data mining. Ieee Access, 2, 1149-1176. https://doi.org/10.1109/ACCESS.2014.2362522
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