Description     What is data mining or data analytic? In your answer, address the following: 1

Subject:Computer SciencePrice:14.89 Bought3

Share With

Description

What is data mining or data analytic? In your answer, address the following:

1. Is it another hype?

2. Describe the steps involved in data mining or data analytics when viewed as a process of knowledge discovery.

3.Is it a simple transformation or application of technology developed from database, statistics, machine learning and pattern recognition?

Your 2 following posts should be commenting on your classmates’ post .

1)QUESTION 1

Data Mining is the process or method for extracting “mines” the interesting information or patterns from large amount of data to be able to take a decision based on that.

Data mining is not an hype rather an important aspect in analyzing data. The need for data mining is due to the wide availability of huge amount of data and the need for transforming such data into a useful information so that we can take a decision or Analysis based on that. So Data mining is the result of evolution of information technology(Rajput, 2020).

QUESTION 2

Rajput (2020) says that data mining knowledge discovery are as follows:-

Data cleaning - a process that removes or transforms noise and inconsistent data - Data integration,

where multiple data sources may be combined.

Data selection - where data relevant to the analysis task are retrieved from the database

Data transformation - where data are transformed or consolidated into forms appropriate for mining

Data mining - an essential process where intelligent and efficient methods are applied in order to extract patterns.

Pattern evaluation - a process that identi.es the truly interesting patterns representing knowledge based on some interestingness measures.

Knowledge presentation - where visualization and knowledge representation techniques are used to present the mined knowledge to the user.

QUESTION 3

No, Data mining is more than just a simple transformation of technology developed from databases, statistics, and machine learning. Its involves integration rather than a simple transformation of techniques from multiple disciplines such as database technology, statistics, machine learning, high performance, computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis(Rajput, 2020).

REFERENCES

Rajput. A. (2020). KDD Process in Data Mining.

2) Data Mining

Data mining is not hype but rather an outcome of the evolving nature of information technology. In essence, data mining is an analytic subgroup that applies mathematical algorithms, including artificial intelligence and machine learning techniques, to inspect enormous datasets and reveal formerly hidden correlations and patterns (Schuh et al., 2019). Currently, the need for data mining keeps rising due to the extensive availability of massive amounts and the impending need to transform such data into valuable knowledge and information.

When seen as a knowledge discovery process, the data mining steps include data cleaning, which is the method that transforms or eradicates inconsistent and noise data. Data integration, which involves combining different data sources, is the next step followed by data selection, whereby only relevant data is regained from the database. The next step is data transformation, which involves consolidating and transforming data into formats suitable for mining. The subsequent step and one of the main steps is data mining, whereby efficient and intelligent methods are used to extract patterns (Qiao & Jiao, 2018). The last two steps include pattern evaluation and knowledge presentation, where the first detects the fascinating patterns and the latter presents the mined knowledge to the user.

Data is not simply a technological transformation created from machine learning, statistics, and databases. Instead, data mining encompasses various techniques from several disciplines such as spatial data analysis, image and signal processing, information retrieval, data visualization, neural networks, pattern recognition, high-performance computing, machine learning, statistics, and database technology. The many database systems that provide transaction and query processing resulted in the dire need for data scrutiny and understanding.