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Customer Data Clustering Using Data Mining Technique
Sankar Rajagopal
International Journal of Database Management Systems , 2011,
Abstract: Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is very important in business process.Large companies are having huge volume of data but starving for knowledge. To overcome the organization current issue, the new breed of technique is required that has intelligence and capability to solve the knowledge scarcity and the technique is called Data mining. The objectives of this paper are to identify the high-profit, high-value and low-risk customers by one of the data mining technique - customer clustering. In the first phase, cleansing the data and developed the patterns via demographic clustering algorithm using IBM I-Miner. In the second phase, profiling the data, develop the clusters and identify the high-value low-risk customers. This cluster typically represents the 10-20 percent of customers which yields 80% of the revenue.
Customer Data Clustering using Data Mining Technique  [PDF]
Dr. Sankar Rajagopal
Computer Science , 2011, DOI: 10.5121/ijdms.2011.3401
Abstract: Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is very important in business process. Large companies are having huge volume of data but starving for knowledge. To overcome the organization current issue, the new breed of technique is required that has intelligence and capability to solve the knowledge scarcity and the technique is called Data mining. The objectives of this paper are to identify the high-profit, high-value and low-risk customers by one of the data mining technique - customer clustering. In the first phase, cleansing the data and developed the patterns via demographic clustering algorithm using IBM I-Miner. In the second phase, profiling the data, develop the clusters and identify the high-value low-risk customers. This cluster typically represents the 10-20 percent of customers which yields 80% of the revenue.
Data Mining Approach to Cervical Cancer Patients Analysis Using Clustering Technique
Kuttiannan Thangavel,P. Palanichamy Jaganathan,P.O. Easmi
Asian Journal of Information Technology , 2012,
Abstract: Data mining is an umbrella term referring to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning (Artificial Intelligence), pattern recognition, neural networks and databases. In particular this paper reveals out how the problem of cervical cancer diagnosis is approached by a data mining analyst with a background in machine learning. Application areas for this problem include analysis of telecommunications systems, discovering frequent buying patterns, analysis of patient`s medical records, etc. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. To the best of our knowledge data mining technique such as clustering has not been used to analyse cervical cancer patients. Hence, in this paper we made an attempt to identify patterns from the database of the cervical cancer patients using clustering.
Merging Data Mining Techniques for Web Page Access Prediction: Integrating Markov Model with Clustering  [PDF]
Triloknath Pandey,Ranjita Kumari Dash,Alkananda Tripathy,Barnali Sahu
International Journal of Computer Science Issues , 2012,
Abstract: Web page access prediction gained its importance from the ever increasing number of e-commerce Web information systems and e-businesses. Web page prediction, that involves personalizing the Web users browsing experiences, assists Web masters in the improvement of the Website structure and helps Web users in navigating the site and accessing the information they need. The most widely used approach for this purpose is the pattern discovery process of Web usage mining that entails many techniques like Markov model, association rules and clustering. Implementing pattern discovery techniques as such helps predict the next page to be accessed by the Web user based on the users previous browsing patterns. However, each of the aforementioned techniques has its own limitations, especially when it comes to accuracy and space complexity. This paper achieves better accuracy as well as less state space complexity and rules generated by performing the following combinations. We integrate low -order Markov model and clustering. The data sets are clustered and Markov model analysis is performed on each cluster instead of the whole data sets. The outcome of the integration is better accuracy than the combination with less state space complexity than higher order Markov model.
Mining Unstructured Data from Web Using Soft Computing  [PDF]
Ms.Sonali A. Deshpande,Dr. P.R.Deshmukh
International Journal of Computer Technology and Applications , 2012,
Abstract: Copious material is available from the World Wide Web (WWW) in response to any user-provided query. It becomes tedious for the user to manually extract real required information from this material. Large document collections, such as those delivered by Internet search engines, are difficult and time-consuming for users to read and analyse. The detection of common and distinctive topics within a document set, together with the generation of multi-document summaries, can greatly ease the burden of information management. In the paper, a technique is proposed called Mining Unstructured Data From Web Using Soft Computing that creates the clusters of web documents using fuzzy clustering which focuses on this problem of mining the useful information from the collected web documents
EXTRACTING IMAGES FROM THE WEB USING DATA MINING TECHNIQUE  [PDF]
Syed thousif hussain,B. N.Kanya
International Journal of Advanced Technology & Engineering Research , 2012,
Abstract: The objective of this work is to generate a large number of images for specified object class. The approach is to em-ploy text, metadata and visual features and to use to gather many high quality images from the web. Candidates images are obtained by text based web search. The web page and the images are downloaded. The task is to remove irrelevant images and to re-rank. First, the images query page is down-loaded. Second, it extracts images URL from downloaded page and place it in the database then ranking is done based on text surrounding and metadata features. SVM and Naive bayes classifier algorithm are compared for ranking. The top ranked images are used as training data and an SVM visual classifier is learned to improve re-ranking. The principal idea of the overall method is in combining text or metadata or visual features in order to achieve a completely automatic ranking of images
An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining  [PDF]
Amreen Khan,,Prof. Dr. N.G.Bawane,,Prof. Sonali Bodkhe
International Journal on Computer Science and Engineering , 2010,
Abstract: Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number ofreal world clustering problems. This paper looks into the use of Particle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and also allows the particles to be robust to trace the changing environment.
An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining
Amreen Khan,,Prof. Dr. N.G.Bawane,Prof. Sonali Bodkhe
International Journal on Computer Science and Engineering , 2010,
Abstract: Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number ofreal world clustering problems. This paper looks into the use ofParticle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and also allows the particles to be robust to trace the changing environment.
Preprocessing and Unsupervised Approach For Web Usage Mining  [cached]
Addanki Ramya,Konda Sreenu,P Ratna Kumar
International Journal of Social Networking and Virtual Communities , 2013, DOI: 10.11591/socnetvircom.v1i2.2197
Abstract: Predicting of user’s browsing behavior is an important technology of E-commerce application. The prediction results can be used for personalization, building proper web site, improving marketing strategy, promotion, product supply, getting marketing information, forecasting market trends, and increasing the competitive strength of enterprises etc. Web Usage Mining is the application of data mining techniques to discover interesting usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining is usually an automated process whereby Web servers collect and report user access patterns in server access logs. The navigation datasets which are sequential in nature. Clustering web data is finding the groups which share common interests and behavior by analyzing the data collected in the web servers, this improves clustering on web data efficiently using proposed robust algorithm. In the proposed work a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism to discover and analyze useful knowledge from the available Web log data.
A Fuzzy Clustering Based Approach for Mining Usage Profiles from Web Log Data  [PDF]
Zahid Ansari,Mohammad Fazle Azeem,A. Vinaya Babu,Waseem Ahmed
Computer Science , 2015,
Abstract: The World Wide Web continues to grow at an amazing rate in both the size and complexity of Web sites and is well on its way to being the main reservoir of information and data. Due to this increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. To design popular and attractive websites publishers must understand their users needs. Therefore analyzing users behaviour is an important part of web page design. Web Usage Mining (WUM) is the application of data mining techniques to web usage log repositories in order to discover the usage patterns that can be used to analyze the users navigational behavior. WUM contains three main steps: preprocessing, knowledge extraction and results analysis. The goal of the preprocessing stage in Web usage mining is to transform the raw web log data into a set of user profiles. Each such profile captures a sequence or a set of URLs representing a user session.
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