1594159500
Orange Data Mining Tool and Association Rules
In this article, association analysis will be studied using the Orange Data Mining tool. The Apriori algorithm will be utilized for creating association rules. Algorithm steps will be shown on a small set of market shopping data.
Association Rules
Association analyses are studies that try to uncover if-else rules hidden within the dataset. It usually yields good results with categorical data. The most common example on association analysis is basket analysis. In addition, it has a wide range of uses such as bioinformatics, disease diagnosis, web mining and text mining.
Basket Analysis
In basket analysis, we keep products bought by shoppers in a list, and wonder which products are sold more together.
The Data
Let’s say we have a data consisting 5 transactions in a market like:
1 Bread, Milk
2 Bread, Tea, Coffee, Eggs
3 Milk, Tea, Coffee, Coke
4 Bread, Milk, Tea, Coffee
5 Bread, Milk, Tea, Coke
We can see that most shoppers who buy Tea also buy Coffee in the dataset. Now, let’s show the dataset using one-hot encoding. The dataset can be downloaded from here.
One-hot encoding
Some Definitions on Association Rules
**Product list: **List of all products in the basket, i.e {Bread, Milk, Eggs}.
**Support count (σ): **The number of items passed on purchases, i.e. σ({Milk, Tea, Coffee}) = 2
Support rate(s): The proportion of the product list in the exchange, i.e. s({Milk, Tea, Coffee}) = 2/5
**Product list frequency: **Support rate list of products above a specific value.
There is more information here on association rules. In this blog, I will show how to utilize association rules using Orange tool.
Apriori Algorithm
The Apriori Algorithm is the most used algorithm in basket analysis. The algorithm starts by specifying a threshold value. For example, let’s take the minimum support threshold to 60%.
Step 1: Type product lists in frequency and identify the product with maximum frequency. Multiply the number of products by threshold value and remove products below the value you find.
Step 2: Multiply the number of products by threshold value and remove products below the value you find.
#associate-rules #apriori #orange-data-mining #data-mining #association #data analysis
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1594159500
Orange Data Mining Tool and Association Rules
In this article, association analysis will be studied using the Orange Data Mining tool. The Apriori algorithm will be utilized for creating association rules. Algorithm steps will be shown on a small set of market shopping data.
Association Rules
Association analyses are studies that try to uncover if-else rules hidden within the dataset. It usually yields good results with categorical data. The most common example on association analysis is basket analysis. In addition, it has a wide range of uses such as bioinformatics, disease diagnosis, web mining and text mining.
Basket Analysis
In basket analysis, we keep products bought by shoppers in a list, and wonder which products are sold more together.
The Data
Let’s say we have a data consisting 5 transactions in a market like:
1 Bread, Milk
2 Bread, Tea, Coffee, Eggs
3 Milk, Tea, Coffee, Coke
4 Bread, Milk, Tea, Coffee
5 Bread, Milk, Tea, Coke
We can see that most shoppers who buy Tea also buy Coffee in the dataset. Now, let’s show the dataset using one-hot encoding. The dataset can be downloaded from here.
One-hot encoding
Some Definitions on Association Rules
**Product list: **List of all products in the basket, i.e {Bread, Milk, Eggs}.
**Support count (σ): **The number of items passed on purchases, i.e. σ({Milk, Tea, Coffee}) = 2
Support rate(s): The proportion of the product list in the exchange, i.e. s({Milk, Tea, Coffee}) = 2/5
**Product list frequency: **Support rate list of products above a specific value.
There is more information here on association rules. In this blog, I will show how to utilize association rules using Orange tool.
Apriori Algorithm
The Apriori Algorithm is the most used algorithm in basket analysis. The algorithm starts by specifying a threshold value. For example, let’s take the minimum support threshold to 60%.
Step 1: Type product lists in frequency and identify the product with maximum frequency. Multiply the number of products by threshold value and remove products below the value you find.
Step 2: Multiply the number of products by threshold value and remove products below the value you find.
#associate-rules #apriori #orange-data-mining #data-mining #association #data analysis
1618018594
Top 21 Data Mining Tools
Data mining is a world itself, which is why it can easily get very confusing. There is an incredible number of data mining tools available in the market. However, while some might be more suitable for handling data mining in Big Data, others stand out for their data visualization features.
As is explained in this article, data mining is about discovering patterns in data and predicting trends and behaviors. Simply put, it is the process of converting vasts sets of data into relevant information. There is not much use in having massive amounts of data if we do not actually know what it means.
This process encompasses other fields such as machine learning, database systems, and statistics. Additionally, data mining functions can vary greatly from data cleansing to artificial intelligence, data analytics, regression, clustering, etc. Consequently, many tools are being developed and updated to fulfill these functions and ensure the quality of large data sets (since poor data quality results in poor and irrelevant insights). This article seeks to explain the best options for each function and context. Keep reading to find out our 21 top mining tools!
#data science #data #data mining #python data science #data mining tools #r for data science
1620466520
Your Data Architecture: Simple Best Practices for Your Data Strategy
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
1624399200
Top 10 Big Data Tools for Data Management and Analytics
Introduction to Big Data
What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
List of Big Data Tools & Frameworks
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
- Big Data Framework
- Data Storage Tools
- Data Visualization Tools
- Big Data Processing Tools
- Data Preprocessing Tools
- Data Wrangling Tools
- Big Data Testing Tools
- Data Governance Tools
- Security Management Tools
- Real-Time Data Streaming Tools
#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics
1624692167
Top 10 Big Data Tools for 2021!
In today’s tech world, data is everything. As the focus on data grows, it keeps multiplying by leaps and bounds each day. If earlier mounds of data were talked about in kilobytes and megabytes, today terabytes have become the base unit for organizational data. This coming in of big data has transformed paradigms of data storage, processing, and analytics.
Instead of only gathering and storing information that can offer crucial insights to meet short-term goals, an increasing number of enterprises are storing much larger amounts of data gathered from multiple resources across business processes. However, all this data is meaningless on its own. It can add value only when it is processed and analyzed the right way to draw point insights that can improve decision-making.
Processing and analyzing big data is not an easy task. If not handled correctly, big data can turn into an obstacle rather than an effective solution for businesses. Effective handling of big data management requires to use of tools that can steer you toward tangible, substantial results. For that, you need a set of great big data tools that will not only solve this problem but also help you in producing substantial results.
Data storage tools, warehouses, and data lakes all play a crucial role in helping companies store and sort vast amounts of information. However, the true power of big data lies in its analytics. There are a host of big data tools in the market today to aid a business’ journey from gathering data to storing, processing, analyzing, and reporting it. Let’s take a closer look at some of the top big data tools that can help you inch closer to your goal of establishing data-driven decision-making and workflow processes.
Apache Hadoop
Apache Spark
Flink
Apache Storm
Apache Cassandra
…
#big data #big data tools #big data management #big data tool #top 10 big data tools for 2021! #top-big-data-tool