Data Mining, also known as Knowledge Discovery in Data (KDD), is the process of extracting patterns and other useful information from large datasets. With the advancement of data warehousing technology and the proliferation of big data, the adoption of data mining technology has accelerated rapidly in recent decades, assisting businesses in transforming raw data into useful knowledge. Despite the fact that this technology is constantly evolving to process large amounts of data, business leaders continue to face scalability and automation challenges.
In this article, you will gain information about Data Summarization in Data Mining. You will also gain a holistic understanding of the importance of Data Mining, its benefits, data summarization, data types, and the different ways of implementing Data Summarization in Data Mining
Read along to find out in-depth information about Data Summarization in Data Mining.
Table of Contents
What is Data Mining?
Data Mining is defined as the process of extracting information from a large set of data. Basically, it is a process in which insights are derived from data through mining. During the process of mining data, the data is analyzed to discover patterns, show correlations, and also uncover anomalies that will allow you to take measures or steps to either improve your business or stop certain practices that hinder growth.
The data mining process commences with identifying the business goal to be achieved from extraction and then proceeds to the collection of the data. The data is stored in a repository from which it is cleaned and arranged to ensure the removal of multiple/duplicate entries and the addition of missing data. Data mining results in the finding of relevant information that will be useful to organizations in solving problems, predicting trends, discovering new opportunities, finding anomalies, showing correlations, and mitigating risks.
Statistical and mathematical algorithms are used in data mining to unfold these patterns enabling organizations to use this information for market analysis, fraud detection, customer retention, production control, science exploration, etc. Data mining is applicable in various industries including banks, healthcare, retail, manufacturing, sports, etc. to make an informed analysis.
What All are the Benefits of Data Mining?
The benefits of Data Mining are as follows:
- Data mining helps businesses acquire knowledge-based information.
- It is applicable to both new and existing systems.
- Businesses can use data mining to make profitable changes to their operations and production.
- It aids in the prediction of trends and behaviors, as well as the automated discovery of hidden patterns.
- Data Mining is a more cost-effective and efficient solution when compared to other statistical data applications.
- Data mining is a quick process that allows users to quickly analyze large amounts of data.
- It allows data scientists to quickly start automated predictions of behaviors and trends, as well as discover hidden patterns.
What is Data Summarization?
The term Data Summarization can be defined as the presentation of a summary/report of generated data in a comprehensible and informative manner. To relay information about the dataset, summarization is obtained from the entire dataset. It is a carefully performed summary that will convey trends and patterns from the dataset in a simplified manner.
Data has become more complex hence, there is a need to summarize the data to gain useful information. Data summarization has great importance in data mining as it can also help in deciding appropriate statistical tests to use depending on the general trends revealed from the summarization.
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What are Data Types?
A dataset contains observations or cases which can be described as information about entities, in most datasets, each row contains information about an observation. The characteristics of an entity are called variables. These characteristics of an individual can include gender, job title, height, etc. Characteristics with similar attributes can be placed in categories and some with numerical values, arithmetic calculations can be carried out on them. Data is stored in four different types namely categorical or nominal, ordinal, continuous or interval, and discrete.
- Categorical or Nominal: These sets of values do not possess a natural ordering and are made up of various categories. They are usually not numerical values. Some of the examples could be ethnicity, constituency, gender, job title or position, etc.
- Ordinal: These set of values have a natural ordering while maintaining their class of values i.e, the data has categories that go with specific orders or rank. They include none < few < some < many, small < medium < large. These categories help in deciding the encoding strategy to be used for each type of data.
- Continuous or Interval: This set of data has a continuous range of numbers where all data values are possible, fractions of numbers are also considered continuous data values.
- Discrete: These are numerical values such as integers or whole numbers.
Data Summarization in Data Mining
We summarize data to simplify it to quickly identify patterns. This gives a descriptive Data Summarization in Data Mining introduction.
Data Summarization in Data Mining is a key concept from which a concise description of a dataset can be obtained to see what looks normal or out of place. A carefully chosen summary of raw data would convey many trends and patterns of the data in an easily accessible manner. The term ‘data mining’ refers to exactly to this i.e., extracting meaningful information from the raw data. And Data Summarization in Data Mining aims at presenting the extracted information and trends in a tabular or graphical format.
In general, data can be summarized numerically in the form of a table known as tabular summarization or visually in the form of a graph known as data visualization.
The different types of Data Summarization in Data Mining are:
- Tabular Summarization: This method instantly conveys patterns such as frequency distribution, cumulative frequency, etc, and
- Data Visualization: Visualisations from a chosen graph style such as histogram, time-series line graph, column/bar graphs, etc. can help to spot trends immediately in a visually appealing way.
There are three areas in which you can implement Data Summarization in Data Mining. These are as follows:
1) Data Summarization in Data Mining: Centrality
The principle of Centrality is used to describe the center or middle value of the data.
Several measures can be used to show the centrality of which the common ones are average also called mean, median, and mode. The three of them summarize the distribution of the sample data.
- Mean: This is used to calculate the numerical average of the set of values.
- Mode: This shows the most frequently repeated value in a dataset.
- Median: This identifies the value in the middle of all the values in the dataset when values are ranked in order.
The most appropriate measure to use will depend largely on the shape of the dataset.
2) Data Summarization in Data Mining: Dispersion
The dispersion of a sample refers to how spread out the values are around the average (center). Looking at the spread of the distribution of data shows the amount of variation or diversity within the data. When the values are close to the center, the sample has low dispersion while high dispersion occurs when they are widely scattered about the center.
Different measures of dispersion can be used based on which is more suitable for your dataset and what you want to focus on. The different measures of dispersion are as follows:
- Standard deviation: This provides a standard way of knowing what is normal, showing what is extra large or extra small and helping you to understand the spread of the variable from the mean. It shows how close all the values are to the mean.
- Variance: This is similar to standard deviation but it measures how tightly or loosely values are spread around the average.
- Range: The range indicates the difference between the largest and the smallest values thereby showing the distance between the extremes.
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3) Data Summarization in Data Mining: Distribution of a Sample of Data
The distribution of sample data values has to do with the shape which refers to how data values are distributed across the range of values in the sample. In simple terms, it means if the values are clustered around the average to show how they are symmetrically arranged around it or if there are more values to one side than the order. Two ways to explore the distribution of the sample data are graphically and through shape statistics.
To draw a picture of the data distribution graphically, frequency histograms and tally plots can be used to summarize the data.
- Histograms: Histograms are similar to bar charts where a bar represents the frequency of values in the data that correspond to various size classes but the difference is that the bars are drawn without gaps in them to show the x-axis representing a continuous variable.
- Tally plots: A tally plot is a kind of data frequency distribution graph that can be used to represent the values from a dataset.
For shape statistics, skewness and kurtosis can help give values to how central the average is and show how clustered they are around the data average.
- Skewness: This is a measure of how central the average is in the distribution. The skewness of a sample is a measure of how central the average is to the overall spread of values.
- Kurtosis: This is a measure of how pointy the distribution is. The Kurtosis of a sample is a measure of how pointed the distribution is, it shows how clustered the values are around the middle.
Determining the shape of the distribution of your data goes a long way in helping you decide which statistical option to choose from when performing data summarization and subsequent analysis through data mining.
In this article, you have learned about Data Summarization in Data Mining. This article also provided information on Data Mining, its benefits, data summarization, data types, and the different ways of implementing Data Summarization in Data Mining.
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