Data Mining
Introduction:
Mining is the process of analyzing the data and extracting some useful information. Data Mining is the process of going through a large dataset to identify the hidden patterns and the relationship among them. This new information will be helpful for data analysis and machine learning. In other words, Knowledge Discovery or Knowledge Extraction.
Disease prediction such as diabetes prediction, brain tumor detection, cancer prediction, hepatitis prediction, and retina disease prediction. crime rate prediction and phishing detection, hotel recommendation system, book recommendation, image mining, web mining, weather forecasting, and sentiment analysis. Contact us for getting final year data mining projects at an affordable price.
Data Mining Parameters:
The parameters include association rules that look for frequent if/then patterns. Classification changes the organization of data. They look for patterns and classify the data based on them. A sequence is one of the common data structure which means an ordered set of items. Sequence/path analysis looks for the pattern of occurrence of events. Clustering groups data items based on similar characteristics. The similarity within a cluster is more whereas the similarity between the clusters is less. Forecasting helps in predicting the future by identifying certain patterns.
Data Mining Process:
The companies want to collect all the relevant data and save it in their warehouses. After that, they want to store and manage their data either in their data servers or in the cloud. Then provide access to the data analyst to process the data. Select a suitable algorithm for mining the data depending on the output. The common algorithms include classification algorithms, regression algorithms, clustering techniques, etc. Identify the patterns or the relationship among the data and present them in an easily understandable form. Data visualization techniques include graphs, charts, decision trees, etc.
Examples:
Wal-Mart, the retail giant stores all of its data in the data warehouse. The data can be accessed by the analyst to identify some useful patterns. The patterns may be the customer buying patterns, items bought together, most shopped days, etc.
Consider a restaurant that wants to mine their data to identify when to give special offers. It will analyze the data and produce output based on the customer visit and what they order.
In certain grocery shop, they will give you a card that provides certain offers. The main aim is to identify what you buy. Identify the buying patterns so that the company can know when to sell the items and their prices.
Data Mining Functionalities:
Data Characterization: Identify and summarize the general characteristics of a class of data.
Data Discrimination: Compare the features of the data objects of the target class with one or more contrasting classes.
Pattern Identification: Identify the frequent patterns that occur in data.
Association Analysis: Association defines the relationship among data. Analyzing different data to identify the relationship between them.
Classification: Distinguish the data into different classes.
Prediction: Based on the identified pattern, predict the future.
Cluster Analysis: Group the data items when their characteristics are similar.
Outlier Analysis: Outliers are defined as the data points that differ from the usual or general behavior of the data. Remove unusual records in the data.
Data Mining Techniques:
k-means clustering:
Clustering refers to grouping the data items based on similar characteristics. K-means group the data points based on the k value. K refers to the defined number of clusters. It works mainly on unsupervised data.
Algorithm:
Step 1: Determine the value of “k”.
Step 2: Select “c” cluster centers.
Step 3: Select the distance between each data point and cluster centers.
Step 4: Assign the data point to the cluster center whose distance from the center is minimum.
Step 5: Recalculate the new cluster centers and the distance between each data point and the cluster center.
Step 6: If no data points were reassigned then stop otherwise repeat from step 4.
KNN (K-nearest neighbor):
K nearest neighbor, a classification technique that works on supervised data. k refers to the number of closest data samples.
Algorithm:
Let n be the number of data samples. choose a point P randomly.
Step 1: store all the data samples as an array.
Step 2: calculate the distance between each element of the array and the point P.
Step 3: Make a set S with K points based on the smallest distance.
Step 4: Return the label of majority data points in the set S.
Data Mining Tools:
R studio- A programming language for data analysis.
Rapid Miner- A tool for text mining and predictive analysis.
Oracle Data Mining- To make predictions.
Kaggle- A community of data scientists and machine learners that provides competitions to solve complex data science problems.
KNIME- To create data science applications and to understand its workflow.
Advantages:
Ø To predict future trends and analyze customer habits.
Ø To improve customer satisfaction and revenue of the company.
Ø To identify hidden patterns and to extract information.
Ø To improve the marketing strategy of the company.
Applications:
Education: To find the pattern in student behavior. To identify the class of students who needs special attention.
Manufacturing: To identify when the products will become obsolete and maintain them.
Retail: To identify the most bought items and to keep them in most attentive places in shops. To offer certain discounts on products that encourage the customer to buy them.
Banking: To trace the payment history of the customer and decide whether to issue a loan or not.
Super Market: To identify the buying patterns of the customer and to target them with suitable products.
E-commerce: Amazon uses mining techniques to promote suitable products to its customers.
Medical: To predict various diseases based on existing data.
Business: To improve their marketing strategy based on customer feedback or response.
Conclusion:
In conclusion, data mining is used to extract information from raw data. It is used in the case of classification, prediction, regression and cluster analysis. The advantages include improving marketing strategy and customer satisfaction. It has various applications to improve the purchasing pattern of the customer and to improve the business. Wal-Mart and many other giant companies in many industries use data mining to enhance their business.
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