We all know what is Data. Data is information from different –different Resources, data is everywhere.
And Data mining is an extractive knowledge from data that can be used to make effective decision making And is also discovering hidden patterns from already available data.
It is generally a base to decision making systems.
Extraction of interesting (non-trivial, implicit, previously known and unknown and also potentially useful) patterns or knowledge from the huge amount of data. It is similar to query processing or search algorithms.
SOME KEY POINTS OF DATA MINING
- Data mining is not searching or even querying the data. Through it has similarity but its way to different.
- Data mining is applied to various forms of data and is applied from the interesting patterns rather than results from the database.
- Additional tasks such as Data preprocessing, etc. are performed in data mining to get effective results.
ANOTHER MEANING OF DATA MINING
- Finding hidden information in the database.
- It is also called exploratory data analysis, data-driven, and deductive learning which means we are learning and deducing something from the database.
- Extracting meaningful information from the database
WHY Data Mining?
As we all are drowning more and more in data, want to learn more but the main point is that we are starving for knowledge and because of data mining statistics growing in the last 2 years is worth more than the data produced earlier.
There is huge growth in data availability seriously, like from terabytes to petabytes.
And what’s the main thing is that where is the data coming from :
- Social media platforms: Facebook, Instagram, Twitter, etc.
- E-commerce: Amazon, Flipkart, shopclues, eBay, etc.
- Society: news, blogs, media, etc.
- Government: Online records, Aadhar links, Bank transactions, etc.
APPLICATIONS OF DATA MINING
Data mining is initially started as the technique and method to find interesting patterns, now data mining act as a base to machine learning and Artificial intelligence applications worldwide.
It is used in medical fields, for example, Health data mining to find the occurrence or cause of certain critical diseases.
It is also used in Ecommerce for example, used in product recommendations, etc.
Used in web page analysis
Also used in prediction algorithms in the stock market.
DATA IN MINING?
- Data in mining will be a relational database, data warehouse, transactional database.
- Data in mining should be time-series data (data over the period of time for one particular application )
- Data in mining should be text databases such as blogs, tweets, reviews, etc.
- Data in mining should be structured, graph data
- Data in mining should be various IoT sensors data.
DISADVANTAGES OF DATA MINING
- Diversity of data types.
- Efficiency and scalability
- User interaction
- Various mining methodology
- Data mining and society
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