Collaborating Authors

Evaluating and Validating Cluster Results Machine Learning

Clustering is the technique to partition data according to their characteristics. Data that are similar in nature belong to the same cluster [1]. There are two types of evaluation methods to evaluate clustering quality. One is an external evaluation where the truth labels in the data sets are known in advance and the other is internal evaluation in which the evaluation is done with data set itself without true labels. In this paper, both external evaluation and internal evaluation are performed on the cluster results of the IRIS dataset. In the case of external evaluation Homogeneity, Correctness and V-measure scores are calculated for the dataset. For internal performance measures, the Silhouette Index and Sum of Square Errors are used. These internal performance measures along with the dendrogram (graphical tool from hierarchical Clustering) are used first to validate the number of clusters. Finally, as a statistical tool, we used the frequency distribution method to compare and provide a visual representation of the distribution of observations within a clustering result and the original data.

Unsupervised Machine Learning: Clustering Analysis – Towards Data Science


Up to know, we have explored just supervised Machine Learning algorithms and techniques to develop models where the data had label previously known. In other words, our data had some target variables with specific values that we used to train our models. However, when dealing with real-world problems, most of the time, data will not come with predefined labels, so we will want to develop machine learning models that can classify correctly this data, by finding by themselves some commonality in the features, that will be used to predict the classes on new data. In summary, the main goal is to study the intrinsic (and commonly hidden) structure of the data. This techniques can be condensed in two main types of problems that unsupervised learning tries to solve.

Learning the Associations of MITRE ATT&CK Adversarial Techniques Artificial Intelligence

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTP). However, this information would be highly useful for attack diagnosis (i.e., forensics) and mitigation (i.e., intrusion response) if we can reliably construct technique associations that will enable predicting unobserved attack techniques based on observed ones. In this paper, we present our statistical machine learning analysis on APT and Software attack data reported by MITRE ATT&CK to infer the technique clustering that represents the significant correlation that can be used for technique prediction. Due to the complex multidimensional relationships between techniques, many of the traditional clustering methods could not obtain usable associations. Our approach, using hierarchical clustering for inferring attack technique associations with 95% confidence, provides statistically significant and explainable technique correlations. Our analysis discovers 98 different technique associations (i.e., clusters) for both APT and Software attacks. Our evaluation results show that 78% of the techniques associated by our algorithm exhibit significant mutual information that indicates reasonably high predictability.

Hierarchical Correlation Clustering and Tree Preserving Embedding Machine Learning

We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters. We then investigate embedding the respective hierarchy to be used for (tree preserving) embedding and feature extraction. We study the connection of such an embedding to single linkage embedding and minimax distances, and in particular study minimax distances for correlation clustering. Finally, we demonstrate the performance of our methods on several UCI and 20 newsgroup datasets.

Exploring Clustering Algorithms: Explanation and Use Cases -


Clustering (cluster analysis) is grouping objects based on similarities. Clustering can be used in many areas, including machine learning, computer graphics, pattern recognition, image analysis, information retrieval, bioinformatics, and data compression. Clusters are a tricky concept, which is why there are so many different clustering algorithms. Different cluster models are employed, and for each of these cluster models, different algorithms can be given. Clusters found by one clustering algorithm will definitely be different from clusters found by a different algorithm. Grouping an unlabelled example is called clustering. As the samples are unlabelled, clustering relies on unsupervised machine learning. If the examples are labeled, then it becomes classification. Knowledge of cluster models is fundamental if you want to understand the differences between various cluster algorithms, and in this article, we're going to explore this topic in depth.