- Introduction to Data Mining
- What can be Achieved with Data Mining?
- Data Mining Techniques
- What are the Popular Tools to bring Data Mining into Action
Introduction to Data Mining
The world today runs on data but the question remains: How are we sourcing this data? The answer to it is not very simple. Data is often extracted from several sources and then crunched into useful numbers which are then used by companies to make any particular decision. Data Mining refers to the set of techniques and methods which involve the use of multiple software for the extraction and finding of suitable data points.
This involves a lot of research and brainstorming as suitable information has to be mined out of several sources. Data mining is not limited to finding out the sources of data. It majorly involves analyzing the existing databases for new insights as a particular sample set of data can give out a lot of new perspectives of looking at it. Data mining makes use of various statistics principles to trace the relationship between any two given data points. Also, in today’s era, it uses tools like machine learning and artificial intelligence to find out various patterns and trends which become difficult to do manually.
What can be Achieved with Data Mining?
Data mining involves scrutinizing of raw data. This raw data can be anything like the price of a particular commodity at a particular point of time, the data on competitors, data on consumption pattern of a particular market segment, data on what marketing strategies have worked out well in a particular industry, etc. Data mining is also used for making business forecasts and predicting the uncertainties which envelop various business entities working in different sectors.
Also, the trend analysis is one of the major benefits of data mining as these give out patterns which help in analyzing the data faster and better and also gives powerful insights for a better understanding of the consumer base and their behavior such as the purchasing pattern, the kind of goods in which the consumer likes to spend, consumption patterns, frequency of purchases made by the customer, etc. thus leading to a productive and curated strategy to be taken to handle a particular market.
Also, with data mining, various hidden facts can be brought to the attention which will help the business in a lot of ways. For example: If a company is looking forward to entering a particular market segment, it would need a lot of information such as the size of the market, the size of the market which the company can tap, demand for that particular product in a particular area, etc. All this can be found out by hitting the eye and mining the correct and the most relevant data sources.
With the help of data mining, you avoid ambiguity and analyze data to extract information which is relevant to a particular business. With data mining, the operational costs can be brought done to a huge extent and with various automated tools available to mine data, the manpower cost has gone down drastically.
Data Mining Techniques
The data can be mined by using several techniques such as:
- Statistics: Statistics deal with the collection and segmentation of data. Here, the quantitative aspect of data is being taken care of. This is an old technique that makes the trend analysis easy. Statistics bring various measures into the picture like regression, correlation, etc.
- Clustering: Clustering of data is one of the most primitive and important steps in mining data. By this technique, the data is segregated into similar chunks and is divided into various segments which are then analyzed independently and also compared to the other segments thus formed.
- Visualization: The visualization of data is a very important aspect of data mining. You can mine a lot of information from a given set of data but it is of no use when the person for whom the information is meant for is unable to understand it. It sanitizes the data and converts it into an understandable form that serves the purpose of data mining.
- Decision Tree: Here, the data is arranged in the form of a tree showing the hierarchal and chronological relevance of different sets of data. Each branch of the tree is a classification and the data which supports the classification. This makes it easier for the user to make decisions and predictions.
- Association: This technique aims at finding various links between two different sets of data or between various classifications made in the same data set. It establishes a relationship between various variables thus extracting valuable information for analysis and implementation.
- Neural Networks: This is a basic foundation step which is automated. The user does not have to put in a lot of effort into the mining of data using neural networks. It is easy to use.
- Classification: This is one of the most popular techniques used in mining data. Here, there are predefined classifications and models which classify a big set of data. It also brings in the element of other techniques which makes the data mining process a lot easier.
What are the Popular Tools to bring Data Mining into Action?
The commonly used data mining tools have been listed below:
- Rapid Miner: So far, this is one of the best tools which uses data to forecast various information. This tool takes up the JAVA language for receiving instructions and is a very insightful tool for predictive analysis. This tool can be used for a lot of functions like training, business applications, etc.
Advantages: This tool makes use of flow-based programming which makes data visualization much easier. It has tools for statistical analysis which are easy to use. Also, one does not need extensive knowledge of coding before using this product.
Disadvantages: The no coding concept makes it difficult for programmers to comprehend the data. Also, getting a license for this tool can be an expensive affair for small business concerns. The use of Rapid Miner is limited to a particular set of modules.
2. Orange: It is a machine learning software that is component-based and makes data visualization a lot easier. It provides various widgets which analyzes the data and then make it ready for visualization. It has a user engagement platform which is both fun and easy to use.
Advantages: Orange is an open-source data mining platform that can work with both scripts and with ETL workflow. This is one of the simplest tools to operate as it is programmed in Python language which is easier to learn compared to the other programming languages. Also, Orange provides a better classification and segregation of data which makes data mining easy.
Disadvantages: Orange has a limited an exhaustive list of machine learning algorithms which makes it a little less versatile and dynamic. Also, the statistical analysis of data becomes a challenge while using Orange. It has a very limited range of reporting capabilities.
3. Weka: This machine learning software is one of the best tools for analyzing data. This tool also aids in predictive modeling and data visualization. This too is written in the JAVA programming language. It can also provide access to various SQL databases which can be analyzed further.
Advantages: It is an open-source tool that is free for use. This tool is mostly used for developing new machine learning algorithms and can support data files from multiple sources.
Disadvantages: There is an issue with the proper documentation of files which are analyzed. It lacks connectivity with Excel sheets and the non-Java databases. Also, the optimization of parameters poses a huge challenge.
4. Sisense: It is one of the best artificial intelligence and data aggregation platform. It caters to the needs of different organizations based on the size of the company, the sector in which the company operates, etc. It further combines data from multiple sources and saves it for later use. It also generates visual reports which make the understanding even easier.
Advantages: Science has the option of working on-premise and it also has a cloud-based option which makes working easier at all times. It is the best application when the database is quite huge. Also, a user can take snapshots of various data points using the Sisense Elasticube.
Disadvantages: The Elasticube feature of the tool is not quite user-friendly. Also, the application works only when you are connected to the internet. Also, the tool is quite heavy which takes in a lot of time. The costs associated with the application is variable, thus making it an expensive option.
5. Revolution: Commonly known as R, this tool provides an interactive platform for statistical operations and data visualization. It is designed in a way that makes it quite user-friendly. It mines the data quite easily. Also, one could perform quite intricate statistical calculations.
Advantages: It makes use of several statistical functions to analyze the data. Also, it makes the heavy programming quite concise and less cumbersome. It has really good graphics features and elements.
Disadvantages: This tool is good at analysis but not that good with data mining. Also, you need to have an extensive knowledge of any array language to work with this tool.
Data mining has come a long way, evolving at every step. With various tools already in the market and various other tools which are constantly being added up in the list. Data handling and extracting of information was never this easy.
To learn more about how data mining is used in data science, visit the data science course page by Great Learning.