Organizations generate and consume vast volumes of Unstructured Data in multiple formats including audio, video, animations, and many more. Therefore, it becomes essential to process and manage such substantial Unstructured Data to gain meaningful insights. Data Mining is one such process that uses several tools and techniques to convert both Structured and Unstructured Data into meaningful insights.
Processing Structured Data is simpler as compared to Unstructured Data because it consists of only one specified format. However, due to technological advancements, many data mining tools can process Unstructured Data seamlessly, such as Talkwalker Analytics, Orange, RapidMiner, and more.
This article introduces you to Data Mining and explains the steps involved in Data Mining Unstructured Data. Moreover, it will elaborate on the various approaches which you can utilize for processing such data. Read along to learn more about Unstructured Data and learn the simple steps for Data Mining Unstructured Data!
Table of contents
- Understanding the various types of data.
- Understanding the importance of Data Mining
What is Data Mining?
Data mining is a field of computer science that processes and analyzes raw data to extract valuable patterns and correlations hidden under a huge chunk of information. In other words, data mining converts raw data into useful information. It can process data from several databases, data warehouses, web, repositories, and more, and then it is aggregated.
Data mining is also known as the Knowledge Discovery in Data (KDD), as it is the process of discovering and identifying new patterns from massive data. Data mining can work on the target dataset to describe patterns.
Organizations today generate a massive amount of data from different sources and platforms. Since the size of such databases is big, it becomes difficult to search for helpful information while getting started with model building. As a result, organizations use many data mining techniques and complex algorithms to extract specific and useful data.
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Steps in Data Mining Unstructured Data
You can perform Data Mining Unstructured Data using the following 5 steps:
Step 1: Data Cleaning for Data Mining Unstructured Data
Teams in organizations need to clean the data before sending it for further processing. Incomplete or dirty data might lead to poor insights and system failure, which costs time and money. Therefore, it becomes essential for developers to clean the data using several cleaning methods, depending on the organization’s resources. One such data cleaning method is manually filling the missing values, removing duplicate values, and more in the data.
Step 2: Data Reduction for Data Mining Unstructured Data
Data reduction refers to a process that reduces the volume of data and represents it in a smaller volume. There are many data reduction techniques like dimensionality reduction and numerosity reduction used to obtain the reduced representation of datasets. These techniques not only help in reducing the actual data but also maintains the integrity of the information. Therefore, data reduction does not affect the results obtained from the data mining process.
Step 3: Data Transformation for Data Mining Unstructured Data
Data Transformation is a process where engineers transform data into an acceptable format. It converts the raw data into a specific or desired form, which will ease the process of retrieving strategic information. Data Transformation also encompasses data mapping and other data science techniques, including processes like aggregation, normalization, and discretization. Once the transformation is complete, you can move forward with the model building for Data Mining Unstructured Data.
Step 4: Model Building and Pattern Mining for Data Mining Unstructured Data
You can detect interesting behavior or patterns in your data by using association rules, correlations, and more. You can also use deep learning algorithms to classify or cluster datasets depending on the characteristics and similarities of your data.
If the data available is labeled, you can leverage machine learning algorithms of random forests, decision trees, and more to categorize that data. However, if your available data is unlabeled, you can use clustering algorithms like K-means, DBSCAN, centroid-based, distribution-based, density-based, and more.
Step 5: Result Analysis
You need to evaluate and interpret the results obtained once the aggregated data. These results need to be helpful and easily understandable so that the organizations can use them for implementing new strategies and achieving their respective goals.
That’s it! You can now try Data Mining Unstructured Data yourself and enhance your business outputs.
Processing Unstructured Data
In the previous section, you learned about Data Mining Unstructured Data. However, an important aspect to learn while managing such data is, How to understand and analyze Unstructured Data?.
Unstructured Data comes from files, photos, spreadsheets, emails, and social media posts. It does not have a predefined format. Therefore, it becomes difficult to move such data into a target system. The easiest way to process your unstructured data is by moving it to a data lake through ELT (Extract, Load, and Transform) processes. This section will elaborate on the following 2 aspects of data processing:
Ways to Analyze Unstructured Data
The following are different ways to study and analyze Unstructured Data:
Metadata is the data that provides information about data. It plays a vital role in managing, storing, and analyzing unstructured data. For example, when you take a photo using a camera or smartphone, it has other information such as date, time, filename, geolocation, and more associated with it. Since there are no industry standards on metadata, each organization can define its metadata fields based on requirements to indicate the nature of the unstructured data. As a result, metadata helps organizations facilitate data search and analysis.
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2) Natural language processing (NLP)
It is a machine learning methodology that helps users to analyze unstructured data. NLP imitates the ability of the human brain to process natural languages like English, Chinese, Spanish, and more. NLP can detect the meaning of text data using semantics and grammatical relationships.
NLP uses the below models to process unstructured data.
- Tokenization: This model breaks the text into tokens. It cuts the text into sentences and words.
- Stop words removal: This model removes articles and prepositions from the text like ‘the,’ ‘to,’ ‘an’, and more which adds no value to the NLP process.
- Stemming: This model removes affixes or additions to a word root through a prefix before the word or a suffix after the word.
- Lemmatization: This model transforms words into their dictionary format which is called ‘lemma.’ For example, tenses are removed, then ‘teaching’ and ‘taught’ both become ‘teach.’ Lemmatization refers to the word’s context, as the same word can have different words depending on where and how it’s used.
- Topic modeling: This model is used to find a group of words from a collection of documents that best represent the information. It can cluster word groups and similar expressions in the document.
3) Image Analysis
Unstructured data also consists of images. For example, diagnosing medical conditions through analyzing x-rays or MRI images.
Image analysis is the process of converting images into their fundamental components and extracting valuable information. It involves the tasks of finding shapes, removing noise, detecting edges, counting objects, image features, etc.
4) Data Visualization
Data visualization is the graphical representation of data that promotes easier understanding. Techniques in data visualization help viewers quickly gain insights into data. Data visualization display every complex structure in data, which can help people understand their data efficiently.
Data visualization highlights entities like people, companies, or cities appearing in the text. Visualizations are also capable of detecting topics or keywords, identifying concepts, and more.
Practices to Understand Unstructured Data
To understand the unstructured data more precisely, you can use various data analysis methods with the below practices:
1) Setting Clear Goals
It is essential to know your goal for understanding your unstructured data. For example, if a product-based company wants to know reviews from customers, it can collect information from social media and analyze the information. But looking at overall reviews is not enough. You can use the collected data to find the root cause of negative reviews on social media and fix the problems for your customers. Keeping a clear objective can benefit the company from unstructured data.
2) Identifying Data Sources
You can identify different data sources like online review platforms, support emails, and more for collecting unstructured data. For example, if you own an e-commerce website, you can find your product’s specific data using hashtags and keywords from social media posts. You can also search through online review websites and find data associated with your products.
3) Cleaning Up Data
Whenever you are working with unstructured data, data clean-up is the essential aspect of data analysis of machine learning models. You can clean your data by removing the whitespaces, symbols, and more. Another way of cleaning data is by creating relationships between data sources and extracting entities, which will design a structured database for analysis.
In this article, you learned about Data Mining and the steps required for Data Mining Unstructured Data. This article also focuses on the practices to understand unstructured data. You can explore several other techniques such as Neural Networks, Clustering, Decision trees, Long-term memory processing, Association, Outlier detection, Statistical techniques, and more for processing unstructured data. It is essential to process and analyze the unstructured data carefully, as it contains different data patterns that might be useful for organizations to implement new strategies.
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Now, to run queries or perform Data Analytics on your raw data, you first need to export this data to a Data Warehouse. This will require you to custom code complex scripts to develop the ETL processes. Hevo Data can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ sources to Cloud-based Data Warehouses like Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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