DATA MINING TECHNIQUES
Data mining involves using advanced data analysis tools to uncover previously hidden, valid patterns and relationships within large datasets. This process employs a variety of techniques, including statistical models, machine learning methods, and mathematical algorithms such as neural networks and decision trees. Consequently, data mining encompasses both analysis and prediction.
Professionals in this field draw on a range of methods and technologies that intersect machine learning, database management, and statistics. Their work focuses on understanding how to effectively process and derive meaningful insights from vast amounts of data.
1.Classification:
This technique is used to extract significant and relevant information about both data and metadata. It aids in classifying data into distinct categories or classes.
Data mining techniques can be classified by different criteria, as follows:
i. Classification of Data mining frameworks as per the type of data sources mined:
Examples of these classifications include multimedia data, spatial data, text data, time-series data, and data from the World Wide Web, among others.
ii .Classification of data mining frameworks as per the database involved:
This classification is based on the data model utilized, such as object-oriented databases, transactional databases, relational databases, and others.
iii. Classification of data mining frameworks as per the kind of knowledge discovered:
This classification is based on the types of knowledge discovered or the data mining functionalities employed. For instance, it includes discrimination, classification, clustering, and characterization. Some frameworks offer a broad range of data mining functionalities within a single system, while others may focus on specific techniques.
iv. Classification of data mining frameworks according to data mining techniques used:
This classification is based on the data analysis approaches used, such as neural networks, machine learning, genetic algorithms, visualization, statistics, and methods oriented towards data warehouses or databases.
2.Clutering:
Clustering involves organizing information into groups of related objects. While summarizing data into a few clusters can result in the loss of some detailed information, it enhances overall understanding by simplifying the data model. Clustering has its roots in statistics, mathematics, and numerical analysis, where it is used to model data through its clusters.
In the context of machine learning, clusters represent hidden patterns, and the process of discovering these clusters is known as unsupervised learning. The resulting framework helps to conceptualize data.
Practically, clustering is vital in various data mining applications, including scientific data exploration, text mining, information retrieval, spatial database applications, customer relationship management (CRM), web analysis, computational biology, medical diagnostics, and more.
3.Regression:
Regression analysis is a data mining process used to identify and analyze the relationships between variables, particularly in the presence of other influencing factors. It helps determine the probability of a specific variable based on these relationships. Essentially, regression is a method for planning and modeling. For instance, it can be used to project costs by considering factors such as availability, consumer demand, and competition. It provides precise relationships between two or more variables within a given dataset.
4. Association Rules:
This data mining technique is designed to uncover connections between two or more items by identifying hidden patterns within a dataset.
Association rules are if-then statements that reveal the likelihood of interactions between data items in large datasets across various types of databases. Association rule mining has numerous applications and is frequently used to analyze sales correlations or to explore patterns in medical datasets.
5. Outer Detection:
This data mining technique focuses on identifying data items that deviate from expected patterns or behaviors. Known as Outlier Analysis or Outlier Mining, this method detects data points that significantly differ from the rest of the dataset. Outliers are common in real-world datasets, and detecting them is crucial for various applications. Outlier detection is particularly useful in fields such as intrusion detection, fraud detection, network interruption identification, and the analysis of data from wireless sensor networks.
6.Sequential Pattern:
Sequential pattern mining is a data mining technique focused on analyzing sequential data to uncover patterns over time. This technique involves identifying noteworthy subsequences within a set of sequences, where the significance of a sequence can be assessed based on various criteria such as length, frequency of occurrence, and other relevant factors.
7.Prediction:
Prediction leverages a combination of data mining techniques, including trends, clustering, and classification. By analyzing past events or instances in their correct sequence, it aims to forecast future occurrences.
