Please answer the multiple choice question below: In March 2015, the copilot Lub
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Question
Please answer the multiple choice question below:
In March 2015, the copilot Lubitz crashed Germanwings Flight 4U9525in the French Alps, killing all 150 people on board. Data shows that in the flight into Barcelona prior to the doomed flight, Lubitz practiced a controlled descent, where he toyed with the plane's settings, programming it for sharp descent multiple times. The "selected altitude" of the flight changed repeatedly, including several times being set as low as 100 feet above the ground. Lubitz also put the engines on idle, which gives the plane the ability to quickly descend. It is highly unusual for a pilot to do these. Which of the following data mining techniques could have been most likely used to prevent the tragedy? Decision tree classification algorithms. Association rule mining. Clustering algorithms. Outlier detection algorithms.Explanation / Answer
The basic algorithm for decision tree is the greedy algorithm that constructs decision trees in a top-down recursive divide-and-conquer manner. A decision tree represents a procedure for classifying categorical data based on their attributes.
We usually employ greedy strategies because they are efficient and easy to implement, but they usually lead to sub-optimal models. It is also efficient for processing large amount of data, so often used in data mining application.
A bottom-up approach could also be used. The construction of decision tree does not require any domain knowledge or parameter setting, and therefore appropriate for exploratory knowledge discovery. It is a recursive divide-and-conquer algorithm. Decision tree learning is one of the most widely used and practical methods for inductive inference over supervised data
Their representation of acquired knowledge in tree form is intuitive and easy to assimilate by humans.
Advantages
Disadvantages
2) Association rule mining
Association rule mining is primarily focused on finding frequent co-occurring associations among a collection of items. The goal is to find associations of items that occur together more often than you would expect from a random sampling of all possibilities.
Association rules are created by analyzing data for frequent if/then statements patterns between seemingly unrelated data and using the criteria support and confidence to identify the most important relationships. Support is an indication of how frequently the items appear in the database. Confidence indicates the number of times the if/then statements have been found to be true.
Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction
3) Clustering Algorithms:
A loose definition of clustering could be the process of organizing objects into groups whose members are similar in some way. Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data.
Two types:
Distance based clustering.
Two or more objects belong to the same cluster if they are close according to a given distance.
Conceptual clustering
Two or more objects belong to the same cluster if this one defines a concept common to all that objects
Clustering algorithm to be advantageous and beneficial some of the conditions need to be satisfied.
4) Outlier detection algorithms
The outlier detection is searching for objects in the database that do not obey laws valid for the major part of the data. The identification of an object as an outlier is affected by various factors, many of which are of interest for practical applications.
Outlier detection is a critical task in many safety critical environments as the outlier indicates abnormal running conditions from which sign cant performance degradation may well result, such as an aircraft engine rotation defect or a problem in a pipeline. An outlier can denote an anomalous object in an image such as a land mine.
List of applications that utilize outlier detection is:
The outlier detection algorithms could have been used to prevent the tragedy