Knowledge Discovery in Databases

KDD or Knowledge Discovery in Databases is a combination of various techniques that are used to extract relationships/patterns in large databases. Knowledge discovery is used to get useful information from any type of data.

Basics of KDD

KDD uses Machine learning, Artificial intelligence and deep learning to discover trends in a dataset to find solutions and answers to real world problems. With the explosion of data around various domains, the use of KDD is on the rise.

Various pattern recognition techniques can be used to discover valuable information in large volumes of data. KDD is often clustered with data warehouses, Data mining and OLAP. The results published by KDD must be easy to understand.

KDD can analyze large amounts of data in a less amount of time with high accuracy. The huge volumes of data that can be transformed using KDD surpass the abilities of all traditional database systems. The knowledge acquired through KDD always bears useful results to its user.

KDD also focuses on automated data extraction processes. These are then used to solve a business problem. Some tools such as Alice, CN2, Darwin, DBMiner, Weka etc. are available for data mining and knowledge discovery in Databases.

Knowledge Discovery in Databases-Steps

1. Selection

It is used to deleting data based on a segmentation criterion.

2. Preprocessing

Preprocessing is a crucial step in the KDD process. Common process in this step includes data cleansing, data normalization and data rationalization.

3. Transformation

Transformation is a very important step in KDD as it generalises the data representation for further processes.

4. Extraction

Process of extracting patterns from data stores to turn it into knowledge

5. Evaluation 

Evaluation is the final step in KDD which is used to evaluate the extracted patterns from the data warehouse.

Important Features of KDD

Here are some prominent features in Some Features of KDD:

1. Deals with huge volumes of data.

2. The results of KDD produce great insights about the data

3. High Level Language is used to carry out KDD processes.

4. Accuracy plays an important role in Knowledge Discovery

Techniques used in Knowledge Discovery in Databases

Various learning data algorithms can be used in KDD. Prior knowledge of statistics and probability is an add-on for better KDD implementation for any developer. Hybrid techniques can also be used in which multiple approaches are combined and used as one. Other than the below mentioned techniques, other methods such as genetic algorithms, data cleaning, pattern discovery, logical deduction etc. can also be used.

1. Statistics and Probability

These techniques involves with the incorporation of various probability and statistical techniques to find patterns in large amount of data. The data is modelled over various probability features such as data interdependencies and capabilities. The outcomes achieved from probability is said to be the discovered knowledge from the data. In statistical methods, a relationship is created between data points. OLAP or On-Line Analytical Processing is also well known for its statistical approach.

2. Trend Analysis

Trend Analysis is an ideal technique in KDD which includes pattern detection by finding various trends using techniques such as filtering. Trend analysis is highly used in large continuous data problems such as telecommunication and stocks. Other applications that can be appropriate for KDD are in traffic data analysis. It can also be used on high volume data such as astronomical and space data. 

3. Classification Analysis

Classification Analysis is one of the most important and widely known methods for KDD. This technique groups data of a similar kind into a class. There can be many types of classification approaches to analyse a dataset. Some of them are described here:

 Domain Tree Method

Domain Tree Method uses acyclic graphs to classify data based on various attributes on which the classification rules are based. As all classification methods, this is also a supervised learning technique. Various predictive models are based on this technique.

 Bayesian method

Naive Bayes probability is used to create a graphical model in this type of classification technique. Bayesian networks are used whenever there a notion of uncertainty is involved in pattern analysis. Hidden Markov Models are also based on this method.

Some other techniques such as artificial neural networks can also be used for pattern recognition and classification.

Applications of Knowledge Discovery in Databases

Text pattern detection, web mining are among the techniques that are used in KDD.

KDD will definitely gain pace over the coming years as its applications increase at a fast pace. It will be used in various intelligent data systems by integrating it with spreadsheets.

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