Updated March 18, 2023
What is Data Mining Algorithm?
Data Mining Algorithms are a particular category of algorithms useful for analyzing data and developing data models to identify meaningful patterns. These are part of machine learning algorithms. These algorithms are implemented through various programming like R language, Python, and data mining tools to derive the optimized data models. Some of the popular data mining algorithms are C4.5 for decision trees, K-means for cluster data analysis, Naive Bayes Algorithm, Support Vector Mechanism Algorithms, The Apriori algorithm for time series data mining. These algorithms are part of data analytics implementation for business. These algorithms are based upon statistical and mathematical formulas which applied to the data set.
Top Data Mining Algorithms
Let us have a look at the top data mining algorithms:
1. C4.5 Algorithm
Some constructs are used by classifiers which are tools in data mining. These systems take inputs from a collection of cases where each case belongs to one of the small numbers of classes and are described by its values for a fixed set of attributes. The output classifier can accurately predict the level to which it belongs. It uses decision trees where the first initial tree is acquired by using a divide and conquer algorithm.
Suppose S is a class and the tree is leaf labelled with the most frequent type in S. Choosing a test based on a single attribute with two or more outcomes than making this test as root one branch for each work of the test can be used. The partitions correspond to subsets S1, S2, etc., which are outcomes for each case. C4.5 allows for multiple products. C4.5 has introduced an alternative formula in thorny decision trees, which consists of a list of rules, where these rules are grouped for each class. To classify the case, the first class whose conditions are satisfied is named as the first one. If the patient meets no power, then it is assigned a default class. The C4.5 rulesets are formed from the initial decision tree. C4.5 enhances the scalability by multi-threading.
2. The k-means Algorithm
This algorithm is a simple method of partitioning a given data set into the user-specified number of clusters. This algorithm works on d-dimensional vectors, D={xi | i= 1, … N} where i is the data point. To get these initial data seeds, the data has to be sampled at random. This sets the solution of clustering a small subset of data, the global mean of data k times. This algorithm can be paired with another algorithm to describe non-convex clusters. It creates k groups from the given set of objects. It explores the entire data set with its cluster analysis. It is simple and faster than other algorithms when it is used with different algorithms. This algorithm is mostly classified as semi-supervised. Along with specifying the number of clusters, it also keeps learning without any information. It observes the group and learns.
3. Naive Bayes Algorithm
This algorithm is based on Bayes theorem. This algorithm is mainly used when the dimensionality of inputs is high. This classifier can easily calculate the next possible output. New raw data can be added during the runtime, and it provides a better probabilistic classifier. Each class has a known set of vectors that aim to create a rule that allows the objects to be assigned to classes in the future. The vectors of variables describe the future things. This is one of the most comfortable algorithms as it is easy to construct and does not have any complicated parameter estimation schemas. It can be easily applied to massive data sets as well. It does not need any elaborate iterative parameter estimation schemes, and hence unskilled users can understand why the classifications are made.
4. Support Vector Machines Algorithm
If a user wants robust and accurate methods, then Support Vector machines algorithm must be tried. SVMs are mainly used for learning classification, regression or ranking function. It is formed based on structural risk minimization and statistical learning theory. The decision boundaries must be identified, which is known as a hyperplane. It helps in the optimal separation of classes. The main job of SVM is to identify the maximizing the margin between two types. The margin is defined as the amount of space between two types. A hyperplane function is like an equation for the line, y= MX + b. SVM can be extended to perform numerical calculations as well. SVM makes use of kernel so that it operates well in higher dimensions. This is a supervised algorithm, and the data set is used first to let SVM know about all the classes. Once this is done then, SVM can be capable of classifying this new data.
5. The Apriori Algorithm
The Apriori algorithm is widely used to find the frequent itemsets from a transaction data set and derive association rules. To find frequent itemsets is not difficult because of its combinatorial explosion. Once we get the frequent itemsets, it is clear to generate association rules for larger or equal specified minimum confidence. Apriori is an algorithm which helps in finding routine data sets by making use of candidate generation. It assumes that the item set or the items present are sorted in lexicographic order. After the introduction of Apriori data mining research has been specifically boosted. It is simple and easy to implement. The basic approach of this algorithm is as below:
- Join: The whole database is used for the hoe frequent 1 item sets.
- Prune: This item set must satisfy the support and confidence to move to the next round for the 2 item sets.
- Repeat: Until the pre-defined size is not reached till, then this is repeated for each itemset level.
Conclusion
With the five algorithms being used prominently, others help in mining data and learn. It integrates different techniques including machine learning, statistics, pattern recognition, artificial intelligence and database systems. All these help in analyzing large sets of data and perform other data analysis tasks. Hence they are the most useful and reliable analytics algorithms.
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