A Frame Work For Clustering Concept Drifting Categorical Data

In cluster sampling, instead of selecting all the subjects from the entire population right off, the researcher must take several steps in order to gather his or her sample population.

If the number of clusters generated for example is changed, it might represent some changes in the dynamics of the arriving stream. Dynamics of data streams using changes in the knowledge structures generated would benefit many temporal-based analysis applications.

Based on these trends, large amount of data are being gathered and stored in databases, and data warehouses. The huge volume and fast pace made the power of data much stronger than what we expected, with lots of potential waiting us to maintain, explore and make decisions about. Using the efficient way to analyze the most helpful and valuable data, as well as to find out the hidden data is becoming urgent and important. Because of these needs, data mining started to be used as a helpful technology, and plays an important role under today’s studying and working environment.

Partitioning methods: This algorithm groups dataset into q clusters, where q is a predefined parameter.

Data mining is defined as the examining of large databases of information in order to generate new information. Nearly every transaction or interaction leaves a data signature that is captured and stored. Data mining is a means of automating the process of analyzing the patterns of data according to different categories. The information is then sorted, collected and assembled into data warehouses for more efficient analysis by algorithms and used to facilitate business decisions. Sophisticated mathematical algorithms are used to segment the data and evaluate the probability of future events.

The data stored in data warehouse will not provide any benefit to the organization until the hidden information is extracted from it. There are various way to extract the data, however data mining is the best method to get the meaningful trends and patterns from the data warehouse. Therefore, data mining can be defined as the method of extracting the valid, comprehensible and previously unknown information from the huge storage to use on the decision making process (P. 1233).

For any given point in a high dimensional data space, the expected gap between Euclidean distance to the closest neighbor and to the farthest point in the space is minimized as the dimensionality grows [5]. Therefore, the notion of nearest neighbor is meaningless. This may lead many data mining tasks, like clustering, to become largely ineffective, as the model becomes more and more susceptible to noise present within the data.

Mohamed Medhat Gaber, Arkady Zaslavsky and Shonali Krishnaswamy. Illustrated that the theoretical foundations of data stream analysis discussed. Mining data stream systems, techniques are critically reviewed. Finally, the research problems in streaming mining field of study are discussed. These research issues should be addressed in order to realize robust systems that are capable of fulfilling the needs of data stream mining applications. The main aim is to explore the data for testing a specific hypothesis. The machine learning field came into existence with advancement in computing power. So, the goal is to achieve efficient solutions to data analysis problems. There are some issues regarding data stream mining discussed such as ‘Handling the continuous flow of data streams.’, ‘Unbounded

data is also Growing. It has resulted large amount of data stock in databases , depot and other repositories . therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining

* Clustering analyzes data objects without consulting a known class label. The objects are clustered or grouped based on the principle of

- With the increment in IT (Information Technology) the size of the databases generated by the organizations due to the availability of low-cost store and the development in the data pick technologies is also increasing , Data mining (DM) also called KDD (Knowledge discovery in databases) helps to identifying priceless information in such large databases. This valuable information can help the decision producer to make exact futurity decisions, In Figure 1 describe data mining techniques

Abstract— Data mining is logical process that is used to extract or “mining” large amount of data in order to find useful data [2]. Knowledge discovery from Data or KDD is synonym for Data Mining[13].There are many different types of techniques that can be used to retrieve information from large amount of data. Each type of technique will generate different results. The type of data mining technique that should be selected depends on the type of business problem that we are trying to solve.

Abstract - This paper presents the analysis of Kmeans and K-Medians clustering algorithm in detecting outliers. Clustering is generally used in pattern recognition where if a user wants to search for some particular pattern, clustering reduces the searching load. The k-means clustering and kmedians clustering algorithm’s performance in detecting outliers are analysed here. K-means clustering clusters the similar data with the help of the mean value and squared error criterion. Kmedians is similar to k-means algorithm but median values are calculated there. Outliers are the one different from norm. If they are not properly detected and handled, they clustering will be affected in a great manner.

Fuzzy clustering plays an important role in feature analysis, system identification and classifier design [21]. Given its capacity for managing uncertainty, impreciseness and vagueness, the fuzzy algorithm is far more realistic for solving real-world problems than hard clustering algorithms. The fuzzy C-mean (FCM) clustering algorithm [22] is a well-known method based on fuzzy clustering. In this method, the image is represented in different feature spaces and the FCM classifies similar data points according to the distance of the pixel from the center of the feature space.

In every day live, the word ‘Mining’ refer to the process that discovered a small set of valuable pieces from a great deal of raw material as in mining process of gold from rocks or sand. According to [3] Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the process of extraction of implicit information that previously unknown and potentially useful from database. By using a number of different technical, such as clustering, data summarization, learning classification, finding dependency networks, analyzing changes, and detecting anomalies. Data Mining refers to a variety of techniques that can be used to analyses and observes database in order to find relationships or summarize the data in ways that can be put to use in different areas such as decision making, prediction and estimation and to do that there are a sequence of the process [2] . As show in figure (1.1)