Define Info Loop

Weka Software Weka Software- Weka software are used for calculating result with different condition in data mining. The weka software is play important role in industry, That software are open source and gives great graphical user interface to user. That software are basically Java based customized tool. The weka software are mainly used in machine learning programs for calculating a different task. In weka software including lots of data mining algorithms for performing a different task.  Weka software gives four types of GUI for work-  1. Simple CLI- It gives simple command line interface that allows direct execution of commands.  2. Explorer-It is an environment for exploring data means display data to user in different format.  3. Experimenter-That section for performing experiment and conducting statistical test between learning schema. In that stage execution of algorithm are perform.  4. Knowledge Flow-It is Java bas...

R Software In data mining lots of software’s are used for predict a values for future. The R software is most popular software used in data Mining industry. That software are runs on Windows operating system, Linux and Mac operating system also. The R software are used in different industries including government, finance, insurance, medicine, scientific research and many more. It is advanced technique for calculating a data mining task. It is a free software environment for statistical computing and graphics in solving different problems from industry. R software can be easily extended 6600 + packages in industry. R software can used in- 1. Machine Learning 2. Statistical Learning 3. Time Series Analysis 4. Cluster Analysis Features of R software- 1. R software has many statistical functions and visualize. 2. The immediate result for user in the format of JPEG, PNG and PDF etc. The R software can gives multi functionality to user. 3. R software is...

K-Means Clustering Algorithm and K-Medoids clustering K-Means Clustering Algorithm  K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this topic, we will learn what is K-means clustering algorithm, how the algorithm works, along with the Python implementation of k-means clustering. What is K-Means Algorithm? K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on. It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way that each dataset belongs only one group that has similar properties. It is a centroid-based algorithm, where each cluster is associated with a ...

Applications and Types of Cluster Analysis •It is widely used in image processing, data analysis, and pattern recognition. •It helps marketers to find the distinct groups in their customer base and they can characterize their customer groups by using purchasing patterns. •It can be used in the field of biology, by deriving animal and plant taxonomies and identifying genes with the same capabilities. •It also helps in information discovery by classifying documents on the web. Advantages of Cluster Analysis: 1.It can help identify patterns and relationships within a dataset that may not be immediately obvious. 2.It can be used for exploratory data analysis and can help with feature selection. 3.It can be used to reduce the dimensionality of the data. 4.It can be used for anomaly detection and outlier identification. 5.It can be used for market segmentation and customer profiling. Disadvantages of Cluster Analysis: 1.It can be sensitive to the choice...

CLUSTER ANALYSIS Cluster analysis, also known as clustering, is a method of data mining that groups similar data points together. The goal of cluster analysis is to divide a dataset into groups (or clusters) such that the data points within each group are more similar to each other than to data points in other groups. This process is often used for exploratory data analysis and can help identify patterns or relationships within the data that may not be immediately obvious. There are many different algorithms used for cluster analysis, such as k-means, hierarchical clustering, and density-based clustering. The choice of algorithm will depend on the specific requirements of the analysis and the nature of the data being analyzed. Cluster Analysis is the process to find similar groups of objects in order to form clusters. It is an unsupervised machine learning-based algorithm that acts on unlabelled data. A group of data points would comprise together t...

Rule-based Classification in Data Mining Rule-based classification in data mining is a technique in which class decisions are taken based on various “if...then… else” rules. Thus, we define it as a classification type governed by a set of IF-THEN rules. We write an IF-THEN rule as: “IF condition THEN conclusion.” IF-THEN Rule To define the IF-THEN rule, we can split it into two parts: •Rule Antecedent: This is the “if condition” part of the rule. This part is present in the LHS(Left Hand Side). The antecedent can have one or more attributes as conditions, with logic AND operator. •Rule Consequent: This is present in the rule's RHS(Right Hand Side). The rule consequent consists of the class prediction. 

various Issues regarding Classification and Prediction in data mining There are the following pre-processing steps that can be used to the data to facilitate boost the accuracy, effectiveness, and scalability of the classification or prediction phase which are as follows − Data cleaning- This defines the pre-processing of data to eliminate or reduce noise by using smoothing methods and the operation of missing values (e.g., by restoring a missing value with the most generally appearing value for that attribute, or with the best probable value established on statistics). Although various classification algorithms have some structures for managing noisy or missing information, this step can support reducing confusion during learning. Relevance analysis- There are various attributes in the data that can be irrelevant to the classification or prediction task. For instance, data recording the day of the week on which a bank loan software was filled is im...

Em Algorithm(Expectation Maximization) And Hierarchical Cluster Em algorithm(Expectation Maximization)- The EM algorithm is the extension of K means algorithm. The EM algorithm is assign each object to a cluster according their weight representation. The probability are mention here for clustering definition they are based on weighted and measures of objects. Hierarchical Method Cluster- It is working for grouping data objects into cluster. It is divided into two types- 1.Aaglomerative Hierarchical Clustering 2.Divisible Hierachical Clustering 1.Aaglomerative Hierarchical Clustering- It is fallow bottom-up strategy. In that merging a small atomic cluster into larger cluster. That process is repeated until the termination condition holds. 2.Divisible Hierachical Clustering- It follows the top-down strategy and it is Reverse process of Aaglomerative hierarchical clustering. That is starting with all objects with one cluster and subdivided cluster into...

K Means Algorithm Partitioning Method- In partitioning method the the ‘n’ is the object group and that group are combined with group notation ‘K’, where each partition represent one cluster. In partitioning method the cluster is a group of undefined or objects with similar characteristics. There are two classified partitioning method are as follows- 1.K means 2.k mediads K means- k-means is one of the simplest unsupervised learning algorithm. Which follows a easy way and simplest formation for defining clustering from undefined objects. K means algorithm used for divide ‘X’ in different cluster and shows which is suitable for X” and accurate cluster to and denote accurate cluster for predict a value. Advantages of K means algorithm- 1.It is fast and easy to understand. 2.Gives a better result when objects are different from each others. Disadvantages for understanding k-means algorithm- 1.It is unable to handle noisy data. 2.That algorithm are fails...

Clustering- Clustering is a group of of known object but the class name is unknown. In cluster the group of object are created one cluster. In that the class name is not defined because of that clustering also called as unsupervised classification. There is no predefined classes are presented in clustering. The lots of abstract object are collected in clustering and making a group of that object for predict the future outcomes. Example- In biology the clustering are used for deriving a plant formation and their types with their object name like color,size etc. In clustering lots of objects are define the format but making a group of similar object characteristics and gives a proper name for that cluster. Applications of Cluster Analysis- 1.Market Research. 2.Pattern Recognition. 3.Data Analysis and Image Processing. 4.It is also used for discover the distinct proof of customer as compared their purchasing patterns. 5.Clustering also used in biological...

Discretization in data mining Data discretization refers to a method of converting a huge number of data values into smaller ones so that the evaluation and management of data become easy. In other words, data discretization is a method of converting attributes values of continuous data into a finite set of intervals with minimum data loss. There are two forms of data discretization first is supervised discretization, and the second is unsupervised discretization. Supervised discretization refers to a method in which the class data is used. Unsupervised discretization refers to a method depending upon the way which operation proceeds. It means it works on the top-down splitting strategy and bottom-up merging strategy. Now, we can understand this concept with the help of an example Suppose we have an attribute of Age with the given values Another example is analytics, where we gather the static data of website visitors. For example, all visitors who ...

Prediction And Regression- Prediction- Prediction is the technique use for predict a desired value from desired data set The prediction used regression method for displaying result of predicted values. The predicted can define with 2 methods-In first method the predicted algorithm choose a descriptive data for predict a value and in second method the predicted algorithms select current data for predict a values. The lots of techniques are used for prediction techniques that are as follows- 1.Nearest neighbour 2.Natural network 3.Bayes classifier 4.Decision tree Regression- The regulation means that calculating a predicted values with only on numeric data. Regression used the statistical method or technique for finding prediction values. The regression can use relationship between one or more independent variables and find a final result. The scalability of a regression is depend upon which type of data are in data set. Several software packages are ...

Naive Bayes Classifier- In machine learning the naive bayes classifier is most useful for finding outcomes with probability technique with use of bayes theorem. The new bayes algorithm are also updated for advanced technique in naive bayes classifier. The new buys algorithm is established in 1950 with text frequencies. The naive bayes classifier algorithm is the advanced method for calculating future outcomes in Real world. The naive bayes classifier are highly scalable, accurate for predict a value for future. In naive bayes classifier algorithm gives condition dependencies of that item for find particular predict values. That model is easy to build and particularly useful for very large data set along with simplicity and accuracy, It is highly sophisticated for classification method. The fallowing Equation is used for finding a naive bayes classifier- P(C/X)=P(X/C).P(C)/P(X) In that equation mainly- P(C/X) is the posterior probability of class. P(C...

Bayesian Classification and Bayes Network- Bayesian Classification represent supervised classification method as well as statistical method for calculating classification functions. The Bayesian classification work on probabilities model with different attributes, The probability cross checked at the time of result calculation. It can solve Diagnostic and predictive problems. The Bayesian classification provide the practical learning algorithm and every based knowledge in one solution. Bayesian algorithm best for calculating future outcomes. P(H/X)=P(X/H).P(H)/P(X) That equation fallows for calculating Bayesian classification. Bayes Network- Bayes network shows the possibilities between various variables. The Bayes network allows a subset of the variable conditionally independent device network. Is is a graphical representation of variable conditions that are independent with each other. They gives possibilities of dependent and independent about re...

Classification and Regression Tree (CART)- Classification and Regression Tree module are popularly used for alternatives of any method for regression. It is introduced by beriman at 1984. The CART follows different method for calculating the future outcomes. It is used a binary tree structure with sequential manner and that all sequence are represent a classified data. The variables are divided in tree structure and find a predicted values for future use. The CART also used cross validation for checks accuracy. The CART model is very valuable tool for predicting Modelling and data mining. The all previous tree methodologies suffer from problem including accuracy, greediness, stability at the time of split root. The CART recover all various drawbacks about tree mining and work great. Definition of CART- “Build’s classification or regression trees for numeric attributes means regression are categorical attributes means classification.” The following s...