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Table of contents
1.
Introduction 
2.
Data Mining Algorithms
2.1.
C4.5 Algorithm
2.2.
The k-means Algorithm
2.3.
Naive Bayes Algorithm
2.4.
Support Vector Machines Algorithm
2.5.
The Apriori Algorithm
3.
How to choose the suitable Algorithm for our application?
4.
FAQs
5.
Conclusion
Last Updated: Mar 27, 2024
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Choosing The Right Algorithm in Data Mining

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Introduction 

Data mining predicts outcomes by looking for anomalies, patterns, and correlations in massive data sets. You may use this information to enhance sales, lower costs, strengthen customer connections, reduce risks, and more using a variety of approaches.

It can turn raw data into information that can help organizations flourish by allowing them to make better decisions. Pictorial data mining, text mining, social media mining, online mining, and audio and video mining are only a few examples of data mining.

Various algorithms are developed to be used in data mining; let's understand more about them and see how we can choose an appropriate algorithm for our use case.

Data Mining Algorithms

C4.5 Algorithm

Classifiers, which are data mining tools, use some constructions. These systems take data from a set of cases, each of which belongs to one of a restricted number of classes and is represented by the values of a set of fixed attributes. The output classifier is capable of reliably predicting which level it belongs to. It employs decision trees, with the first tree being obtained via a divide and conquer technique.

Assume S is a class, and the most common type in S is leaf-labelled on the tree. Choosing a test based on a single attribute with two or more outcomes and using this test as the root can result in one branch for each test work. The partitions correspond to the subsets S1, S2, and so on, representing the cases' outcomes. Multiple products are possible with C4.5. In thorny decision trees, C4.5 has given an alternate formula consisting of a set of rules grouped for each class. To categorize the situation, the first-class whose met conditions are designated as the first. If no power is available, the patient is assigned to a default class. The first decision tree is used to create the C4.5 rulesets. Multi-threading in C4.5 improves scalability.

The k-means Algorithm

This Algorithm is a basic method of partitioning a data set into the number of clusters that the user specifies. D=xi | i= 1,... N, where I is the data point, this Algorithm works on d-dimensional vectors. The data must be sampled at random to obtain these initial data seeds. This establishes the global mean of data k times as the answer for clustering a small subset of data. To characterize non-convex clusters, this approach can be combined with another. It divides the supplied set of items into k groups. With its cluster analysis, it looks at the full data set. It is simple and faster than other methods when utilised with other algorithms. Semi-supervised is the most common classification for this Algorithm. Semi-supervised is the most common classification for this Algorithm. It continues to learn without any information and specifying the number of clusters. It observes and learns from the group.

Naive Bayes Algorithm

The Bayes theorem is used in this Algorithm. When the dimensionality of the inputs is high, this Algorithm is utilized. The following probable output can be easily calculated using this classifier. New raw data can be contributed during the runtime, resulting in a more accurate probabilistic classifier. Each class has a set of known vectors to establish a rule that will allow objects to be allocated to classes in the future. The vectors of variables describe what will happen in the future. This is one of the most convenient algorithms because it is simple to implement and has no complicated parameter estimation schemes. It may also be applied to large data sets. It doesn't require any complicated iterative parameter estimate approaches; thus, it's suitable for beginners.

Support Vector Machines Algorithm

The Support Vector Machines algorithm should be tried if a user desires reliable and accurate methods. SVMs are most commonly used to train classification, regression, and ranking functions. It is based on statistical learning theory and structural risk minimization. The decision boundaries, also known as a hyperplane, must be identified. It aids in the most effective division of classes. SVM's main task is to find the best way to maximize the margin between two sets of data. The margin is the amount of space between two sorts of objects. A hyperplane function, y= MX + b, is similar to a line equation. SVM can also be used to carry out numerical calculations. The kernel is used in SVM to allow it to work in more excellent dimensions. This is a unique opportunity. This is a supervised approach in which the data set is utilized to inform SVM about all of the classes. SVM will be able to categorize this new data once this is completed.

The Apriori Algorithm

The Apriori approach is extensively used to locate frequent itemsets and derive association rules from a transaction data set. Because of the combinatorial explosion, finding frequent itemsets is not tricky. It's simple to build association rules for more enormous or equal stated minimal confidence after we have the frequent itemsets. Apriori is a candidate generation-based method that aids in the discovery of routine data sets. It is assumed that the item set or the items in question are in lexicographic order. Data mining research has been boosted significantly after the introduction of Apriori. It is straightforward to apply. This Algorithm's core approach is as follows:

Join: For the most common 1 item collections, the entire database is used.

Prune: To advance to the next round of the two item sets, this item set must gain enough support and confidence.

Repeat: This is done for each item set level till the pre-defined size is not met.

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How to choose the suitable Algorithm for our application?

First, find out the category of the task we want to perform; here are some broad categories of which our use-case is generally part. 

  • Classification algorithms: Based on the other qualities in the dataset, classification algorithms anticipate one or more discrete variables.
  • Regression algorithms: Based on other properties in the dataset, regression algorithms predict one or more continuous numeric variables, such as profit or loss.
  • Segmentation algorithms: Data is divided into groupings, or clusters, by segmentation algorithms, which group things with comparable attributes together.
  • Association algorithms: In a dataset, association algorithms look for relationships between different attributes. This type of Algorithm is most commonly used to create association rules that may be employed in a market basket analysis.
  • Sequence analysis algorithms: Sequence analysis techniques describe common data sequences or episodes, such as a series of website clicks or a series of log events preceding machine repair.

After this has been decided go to the list of various popular algorithms which are used for the category that our task belongs to, and see which one fits our use-case. 

For example, ID3 Algorithm can be used for classification, but it doesn't support discrete attributes. 

Hence we have to choose some algorithm that supports it, and it can be C4.5, or something else. 

Hence the most important step is identifying the category of the step that we want to do, and then once it's identified, we can go to check various algorithms which are available for this kind of tasks and choose the one which is most appropriate and robust according to the data that we have. 

This article lists some algorithms for the same and can be helpful, but there are many more algorithms than listed here and should be taken into consideration.

FAQs

1. How is data mining done?

Data mining is the process of cleaning raw data, detecting patterns, constructing models, and testing such models in order to gain a better knowledge of it. Statistics, machine learning, and database systems are all part of it.

2. What is data mining in databases?

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can be used in a variety of ways, such as database marketing, credit risk management, fraud detection, spam Email filtering, or even to discern the sentiment or opinion of users.

3. What are the two main tasks of data mining?

The two "high-level" primary goals of data mining, in practice, are prediction and description.

4. What is data mining query language?

The Structured Query Language is the foundation for the Data Mining Query Language (SQL). Ad hoc and interactive data mining can be supported by Data Mining Query Languages. This DMQL includes primitive-specific instructions. The DMQL can also be used with databases and data warehouses.

Conclusion

So, in a nutshell, there are various algorithms that can be employed for a particular task but the foremost step is to identify the category to which our task belongs and then choose the best Algorithm which suits our use case in terms of how our input and output is there.

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Also, check out - Anomalies In DBMS.

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