# Terminology:Regression:Regression in minimal value. I am also

Terminology:Regression:Regression is a statistical approach for determining the relation between two attributes.Regression Indetermination coefficient: Using the regression indetermination coefficient regression quality can be determined. Higher the indetermination coefficient lower the quality of regression.Clustering:Clustering is a process of partitioning the data points into meaningful sub-classes called clusters.Clustering Algorithms: Main goal of clustering algorithms is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics, like Volume,Variety and velocity.Introduction:The aim of writing this report is to give a detailed description about a new clustering algorithm that has been introduced in order to deal with partitioning the data-set into clusters under the condition that, the regression indetermination coefficient for each cluster results in minimal value. I am also going to discuss the proposal and implementation of this algorithm. This clustering algorithm is developed with generic programming approach. We use generic algorithms because it provides the sub-optimal solutions. Most of the clustering algorithms try to minimize the inter clustering and intra clustering distances, However, a few applications such as cellular network planning require to decrease the indetermination coefficient. Regression is one of the most widely used techniques for data analysis. It has a wide application range from experimental data to modern data mining. Below I am discussing how regression is helpful in dividing the data into clusters with an example.Idea of considering regression as a clustering method with an Example:The population of the fish has been considered during the ichthyology research. We assume all the fish belongs under one species because it is impossible to describe a few attributes like size, shape, color etc., of fish. Under the condition – “effect of water temperature on the average swimming speed of the fish” we acquire a set of data after several measurements. Now we try to delineate (Figure 1) the data obtained considering speed of fish on Y axis and temperature on X axis and build a function. One of the approaches is to build a linear regression function for the data. After acquiring the function we try to find the relation that exists between the average swimming speed and temperature. From the obtained linear regression function we predict that “as the temperature increases there is a decrease in the average speed of fish” though it results a high error rate because of the residuals.However, there exists another approach called clustering. Having a closer look at the Figure 1, we can come up with an idea of splitting the data (fish population) into two clusters. Now we try to obtain a linear regression functions for two clusters separately until we get a satisfactory (minimized) regression indetermination coefficient instead of getting a rough regression function for the entire data items. After performing the clustering we assume that the population(fish) has two kinds of spices which react differently at different temperatures. The regression functions formed have a regression indetermination coefficient which is nearly equivalent to zero which means that the these functions are a good fit model for this experimental data. Below I have delineated (Figure 2) how well the regression lines fits the two clusters formed.The above discussed procedure works well when the data set is small, with minimized indetermination coefficient that predicts the strength of relationship between two attributes. Unfortunately the data-sets with which we deal in real world i.e., in the data mining applications it is rare to find a small data set, in order to conduct the analysis manually. We cannot just predict the number of clusters that should be formed from the data-set by visualizing, so we require this clustering algorithm to find the accurate relation between attributes. This algorithm considers error rates to measure the quality. Proposal of Algorithm :When the data-set is very huge it is manually impossible to perform visualization and decide the number of number of clusters to be formed as we saw in the example1, so we need an automated system that can predict the number of clusters. Another approach that can be followed is to perform the algorithm repeatedly as we perform in k means clustering and consider the best solution. Regression function parameters fails to determine the distance between the data-set items, but can be used to determine the fitness function which is used as cluster quality measure. To further solve the problem we apply genetic problem approach using the cluster quality measure.Implementation of AlgorithmAssumptions:Let’s assume there are two attributes in the given input data-set and the number of clusters that we have to divide is two. Consider that D is the data-set containing objects {x,y} which have to be clustered.D = d1,d2,d3,d4..,dnd1,d2,d3,d4..,dnAssume U is the binary vector which represents the distribution followed by the objects in D and split them into two sets C1 and C2 clusters.U = U1,U2,U3,.,UnU1,U2,U3,.,UnIf UiUi is 0, didi belongs to C1C1If UiUi is 1, didi belongs to C2C2Let R be a quadruple consisting the coefficients (a,b,c,d) of the formed linear regression functions.(fr1fr1 and fr2fr2) . We can also consider R as a binary vector, by changing the coefficient values to binary.5.We denote P as a set of all possible vectors and name it objects population.6.We denote Q as the set of all vectors of R and name it function population.Using the above mentioned assumptions I am explaining the algorithm in 2 steps.Step1: First we populate the P and Q with random data. Every vector of P is compared with binary vector U and grouped as cluster1(C1C1) and cluster2 (C2C2) based on the binary vector U. Regression functions (f1f1 and f2f2) are computed for both the clusters. Regression Indetermination coefficients(fi1fi1 and fi2fi2) of regression functions are computed respectively. Then we find the quality measure of each cluster with fit function, the cluster with maximum quality is considered. We perform genetic operations on P vector using the fit function. The best individuals are selected from the P and form the regression function(f1f1,f2f2) and the coefficients(fr1,fr2fr1,fr2) are sent into vector R. We insert the vector R values into Q vector.Step2: Using the above formed regression indetermination coefficient (fr1,fr2fr1,fr2) and the data objects of the data-set D we find the Euclidean distance and divide them into clusters C1C1 and C2C2 based on this distance. Now find the regression function(f1,f2f1,f2) and regression indetermination coefficients (fi1,fi2fi1,fi2) and use the fit function to determine the quality. And perform the genetic operation on Q vector and get the individuals into UxUx using the C1C1 and C2C2 distributions for the best individuals from Q. Insert UxUx into P.Both step1 and step2 are performed repeatedly until we reach the satisfactory indetermination coefficients.Finally this algorithm mainly competes two populations, i.e.,Object population P: This contains the individuals evolving the distribution of data-set objects.Function population Q: This contains evolving the regression functions. The best individuals are transferred between these two populations after the conversion.When we have Multiple Dimensions:In a multi-dimensional space it is hard to recognize the linear patterns. In multiple regression function it forms a hyper plane which is hard to visualize. The dependent variable is a vector and the independent variable is a matrix. As the dimensions increase it is polynomially hard to implement using the algorithm. Instead regression approximation algorithm can be used when we are dealing with multiple dimensions.Conclusion:The algorithm discussed above cannot be considered as universal clustering methodology. When dealing with classical regression if unsatisfactory results are obtained this algorithm can be used to show the correlation between attributes. This algorithm is still in it first stage of development. A better way should be found for the automatic selection of clusters. There has be enhancements done to the generic programming approach and fitness function.