of FCM. A suitable kernel function having a key to the success of configuration for kernel method. A single kernel that is choosing from predefined group is sufficient to represent the data. The multiple kernels are combine from the set of basis kernel have adoption for refining the results of single kernel learning. A hierarchical organization is an organizational structure is subordinate to single other entity and it represent in the form of hierarchy. In hierarchy structure consist of singular or group of power at the top level. The members of hierarchical structures are communicate with their instant superior and with their instant subordinate and it can reduce overhead communication for limiting information flow. A modern computational technology which is a method of examining and calculating and estimating the claims about human language itself is known as Natural Language Processing (NLP). Applying NLP to the datamining and textmining previously unknown information can be discovered. The textmining refers to the process of extracting high quality information from text. Documentclustering (or Textclustering) is the process of automatically organizing the documents, extraction of topics and for fast information retrieval or filtering.
Abstract— Clustering is one of the most important datamining or textmining algorithm that is used to group similar objects together. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the correctness of the similarity measure between the data in which the similarity can be computed. This paper focus on implementation of Agglomerative hierarchical clustering with Multiviewpoint based similarity measure for effective documentclustering. The experiment is conducted over sixteen text documents and performance of the proposed model is analysed and compared to existing standard clustering method with MVS. The experiment results clearly shows that the proposed model Hierarchical Agglomerative Clustering with Multiview Point based Similarity Measure perform quite well.
feature selection methods for classification are either supervised or unsupervised, depending on whether the class label information is required for each document. Those unsupervised feature selection methods, such as the ones using document frequency and term strength (TS), can be easily applied to clustering. However, supervised feature selection methods using the information gain and the X 2 statistic can improve the clustering performance better than unsupervised methods when the class labels of documents are available for the feature selection. However, supervised feature selection methods cannot be directly applied to documentclustering because, usually, the required class label information is not available. The Iterative Feature Selection (IF) method is proposed, which utilizes the supervised feature selection to iteratively select features and perform textclustering. In many previous textmining and information retrieval research, the X 2 term-category independence test has been widely used for the feature selection in a separate preprocessing step before text categorization. By ranking their X 2 statistic values, features that have strong dependency on the categories can be selected and this method is denoted as CHI. Two variants of the X 2 statistic have been proposed recently . Correlation coefficient is proposed, which could be viewed as a “one- sided” X 2 statistic. Galavotti et al. went further in this direction and proposed a simplified variant of the X 2 statistic, which was called GSS coefficient. Feature selection methods based on these two variants of the X 2 statistic were tested on improving the performance of text categorization.
Genetic algorithm technique according to the latent semantic model (GAL) to cluster the text was studied. The major constraint is this application of genetic algorithm (GA) to cluster the document is the existing multiples of thousands of dimensions available in feature space that is apt for text. Since many of the straightforward and popular approach signify text with vector space model (VSM) which means that every single term present in the vocabulary signifies dimensions. Latent semantic indexing (LSI) refers to a booming technology in the concept of information retrieval that tries to explore the latent semantics inferred by queries or documents through their representation in a dimension-minimized space. At the mean time, LSI considers the impacts of synonymy and polysemy that construct semantic structures in text. Genetic Algorithm belongs to search technique that efficiently evolves the optimal solutions in the minimized space. This work proposes a variable string length genetic algorithm that is explored for automatic evolve of the exact quantity of clusters and provide best optimal clustering of data. Genetic Algorithm can be applied in combination with the minimized latent semantics structure and enhance clustering effectiveness and accurateness. The supremacy of GAL technique on the traditional GA used in VSM model is given through best Reuter documentclustering result .
Abstract- This paper present that increasing efficiency for document processing is fundamental concept for any organization. All the documents have been processed manually but it seems very difficult if someone needs to have particular information from particular document in a less time. Therefore, parallel comparison is focusing on performance and efficient processing of multiple documents simultaneously. Design of parallel algorithm and performance measurement is the major issue. If one wants the document content to be excess as soon as possible then it require too much time. The major need of this paper is to meet the performance objectives such as time, dataset names, and size of data sets, support value and match score that tell us the whole information about the particular document and also give us the document in the re-rank manner and visualizes this result for easy analysis. There is no any Technique that can handle, manage and retrieve the information as per the user need. So, we used the combination of the clustering and mining technique to prove the result of this parallel comparison and to evaluate the accuracy i.e. stable output in the form of graph. The experimental results show that the proposed parallel comparison algorithm for each Mining, Clustering and for Comparison achieves good performance as compare to sequential.
Textmining, also referred to as textdatamining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Textmining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in textmining usually refers to some combination of relevance, novelty, and interestingness. Typical textmining tasks include text categorization, textclustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
selection is an decision-making task commonly found in government funding agencies, universities, research institutes, For large number of proposals ,it is common to group according to their disciplines. TextMining has emerged as a definitive technique for extracting the unknown information from large textDocument. Ontology is a knowledge repository in which concepts and terms are defined as well as relationships between these concepts. Ontology's make the task of searching similar pattern of text that to be more effective, efficient and interactive. This proposals are then sent to appropriate expert for peer review. Current methods for grouping proposals are based on manual matching of similar research discipline areas or keywords. This paper represents ontology based text –mining approach for clustering proposals based on similarities in research area. This method can be used to improve the efficiency and effectiveness of research proposal selection processes in government and private research agencies. A knowledge based agent is appended to the proposed system for a retrieval of data from the system in an efficient way. This method concerned with optimization model by geographical region.
Text documents are semi structured in nature, i.e., it is neither completely un-structured nor completely structured. A document may include a few structured topics, such as title, abstract and index terms domain etc., and it also contains some huge unstructured textual topics, such as introduction and contents. In recent research on textdocument management, several studies have been done to build and develop semi-structured data. To handle the un- structured documents, text indexing and IR (Information Retrieval) techniques have been developed. But those IR and other traditional techniques are not sufficient to handle vast amount of data . This paper discusses the various techniques and tools have been used for document management in the recent years and finally provide an outline about this proposed work. This survey also handles the important process of documentclustering, which is known as feature discovery process. This relevance feature discovery process helps to identify the useful features available in the text documents at the time of training .
Technically TextMining is a process of deriving high quality information from text. In this the hidden patterns and trends are extracted from the textual data. There are multiple utilities of textmining like- Text Classification, Opinion Mining, TextClustering, and Document Summarization etc. Textmining is that the method of seeking or extracting the helpful info from the matter information. It is an exciting analysis space because it tries to find knowledge from unstructured texts. it's additionally legendary as Textdata processing (TDM) and information Discovery in matter Databases (KDT). KDT plays an more and more important role in rising applications, like Text Understanding. Textmining method is same as data processing, except, the datamining tools ar designed to handle structured data whereas textmining will able to handle unstructured or semi-structured information sets like emails hypertext mark-up language files and full text documents etc. . TextMining is employed for locating the new, previously unidentified info from completely different written resources. Structured information is information that resides in an exceedingly mounted field within a record or file. This information is contained in relational database and spreadsheets. The unstructured information typically refers to info that does not reside in an exceedingly ancient row-column info and it's the other of structured information. Semi Structured data is that the typed information in an exceedingly standard info system. Textmining may be a new space of applied science analysis that tries to resolve the problems that occur within the space of datamining, machine learning, info extraction, linguistic communication process, info retrieval, information management and classification.
Textclustering is one of the main themes in textmining .It refers to the process of grouping document with similar contents or topics into clusters to improve both availability & reliability of the mining, In this research, a frequent item set is a set of words which occur together frequently and are good can- Di Dates for clusters. By considering only the items which occur frequently in the Data, we can also address problems like outlier removal, Dimensionality reduction, etc. The main idea is to apply any existing frequent item finding algorithm such as a priori or Dp-tree to the initial set of text files to reduce the Dimension of the input text files. A Document Feature vector is formed for all the Documents. Then a vector is formed for all the static text input files. The algorithm outputs a set of clusters from the initial input of text files considered. Keywords—Commonality measure; frequent item; Clustering; Apriori
The set of relevant attributes specied may involve other attributes which were not explicitly mentioned, but which should be included because they are implied by the concept hierarchy or dimensions involved in the set of relevant attributes specied. For example, a query-relevant set of attributes may contain city . This attribute, however, may be part of other concept hierarchies such as the concept hierarchy street < city < province or state < country for the dimension location . In this case, the attributes street , province or state , and country should also be included in the set of relevant attributes since they represent lower or higher level abstractions of city . This facilitates the mining of knowledge at multiple levels of abstraction by specialization (drill-down) and generalization (roll-up). Specication of the relevant attributes or dimensions can be a dicult task for users. A user may have only a rough idea of what the interesting attributes for exploration might be. Furthermore, when specifying the data to be mined, the user may overlook additional relevant data having strong semantic links to them. For example, the sales of certain items may be closely linked to particular events such as Christmas or Halloween, or to particular groups of people, yet these factors may not be included in the general data analysis request. For such cases, mechanisms can be used which help give a more precise specication of the task-relevant data. These include functions to evaluate and rank attributes according to their relevancy with respect to the operation specied. In addition, techniques that search for attributes with strong semantic ties can be used to enhance the initial dataset specied by the user.
regular expression patterns that are over-repre- sented in a given set of sequences. The algorithm was applied to discovery patterns both in the complete set of sequences taken upstream of the putative yeast genes and in the regions upstream of the genes with similar expression profiles. The algorithm is able to discover various subclasses of regular expression type patterns of unlimited length common to as few as ten sequences from thousands. In particular, it was able to predict regulatory elements from gene upstream regions in the yeast Saccharomyces cerevisiae. Errors are allowed in the search, represented by wildcard positions. According to our notation, the problem dealt with in Brazma et al. (1998b) consists in dis- covering those patterns satisfying the extraction constraints E c = <2, 1, -, 0, d max , 0, e max >, and such that the concept of interest is related, in this case, to the number of input sequences where the pattern P occurs and also to the specific positions where the errors appear. Those are, in some cases, fixed in the box included in the pattern and restricted to subsets of the alphabet in input (they call such sets of possible symbol substitutions character groups, referring to wildcards of fixed lengths). The paper by Jensen and Knudsen (2000) also deserves mentioning. It proposes two word- analysis algorithms for the automatic discovery of regulatory sequence elements, applied to the Saccharomyces cerevisiae genome and publicly available DNA array data sets. The approach relies on the functional annotation of genes. The aim is the identification of patterns that are over- represented in a set of sequences (positive set) compared to a reference set (negative set). In this case, the authors consider four numbers to decide whether a pattern is significantly overrepresented. The first represents the number of sequences in the positive set that contains the pattern; the second is the number of sequences in the negative set that contains the pattern; the last two denote the number of sequences in each of the two sets that do not contain the pattern. Distributions on such numbers are used to compute the significance
Variation in data creation. This is an issue only when scoring data from a different source than that of the model development data. For example, let's say a model is developed using one list source, and the main characteristics used in the model are age and gender. You might think that another file with the same characteristics and selection criteria would produce similar scores, but this is often not the case because the way the characteristic values are gathered may vary greatly. Let's look at age. It can be self-reported. You can just imagine the bias that might come from that. Or it can be taken from motor vehicle records, which are pretty accurate sources. But it's not available in all states. Age is also estimated using other age -sensitive characteristics such as graduation year or age of credit bureau file. These estimates make certain assumptions that may or may not be accurate. Finally, many sources provide data cleansing. The missing value substitution methodology alone can create great variation in the values.
There are several directions in which this work was projected to evolve. The overall prediction accuracy can be enhanced by adding more relevant variables and by polling human expert opinions into a fused prediction. For instance, if a significant number of users indicate that a model can be improved by including an additional parameter to capture a specific char- acteristic of the problem (e.g., level of correlation between the movie script and the current social/political affairs), then the system should be receptive to such a proposal. Once in use by the decision makers, the fore- casted results obtained over time can be stored and matched (synchro- nized/compared) with the actual box-office data (as soon as they become available) to check how good the forecasts (both individual model predic- tions and the fused ones) had been. Based on the results, the new data can be used to update the parameters of the predictions models, moving to- wards realizing the concept of living models. That is, datamining driven models and subsequent prediction systems should be deployed in environ- ments where the models are constantly tested, validated and (as needed) modified (recalibrated). Such a dynamic (living and evolving) prediction systems are what the decision makers need, and what the datamining technology can provide.
The WEKA (Waikato Environment for Knowledge Analysis) machine learning work- bench is open-source software issued under the GNU General Public License, which includes a collection of tools for completing many datamining tasks. Data Min- ing Methods and Models presents several hands-on, step-by-step tutorial exam- ples using WEKA 3.4, along with input ﬁles available from the book’s compan- ion Web site www.dataminingconsultant.com. The reader is shown how to carry out the following types of analysis, using WEKA: logistic regression (Chapter 4), naive Bayes classiﬁcation (Chapter 5), Bayesian networks classiﬁcation (Chap- ter 5), and genetic algorithms (Chapter 6). For more information regarding Weka, see http://www.cs.waikato.ac.nz/ ∼ ml/. The author is deeply grateful to James Steck for providing these WEKA examples and exercises. James Steck (james email@example.com) served as graduate assistant to the author during the 2004–2005 academic year. He was one of the ﬁrst students to complete the master of science in datamining from Central Connecticut State University in 2005 (GPA 4.0) and received the ﬁrst datamining Graduate Academic Award. James lives with his wife and son in Issaquah, Washington.
The Weka system that illustrates the ideas in this book forms a crucial component of it. It was conceived by the authors and designed and implemented principally by Eibe Frank, Mark Hall, Peter Reutemann, and Len Trigg, but many people in the machine learning laboratory at Waikato made significant early contributions. Since the first edition of this book, the Weka team has expanded considerably: So many people have contributed that it is impossible to acknowledge everyone properly. We are grateful to Remco Bouckaert for his Bayes net package and many other contribu- tions, Lin Dong for her implementations of multi-instance learning methods, Dale Fletcher for many database-related aspects, James Foulds for his work on multi- instance filtering, Anna Huang for information bottleneck clustering, Martin Gütlein for his work on feature selection, Kathryn Hempstalk for her one-class classifier, Ashraf Kibriya and Richard Kirkby for contributions far too numerous to list, Niels Landwehr for logistic model trees, Chi-Chung Lau for creating all the icons for the Knowledge Flow interface, Abdelaziz Mahoui for the implementation of K*, Stefan Mutter for association-rule mining, Malcolm Ware for numerous miscellaneous contributions, Haijian Shi for his implementations of tree learners, Marc Sumner for his work on speeding up logistic model trees, Tony Voyle for least-median-of- squares regression, Yong Wang for Pace regression and the original implementation of M5 ′ , and Xin Xu for his multi-instance learning package, JRip, logistic regression, and many other contributions. Our sincere thanks go to all these people for their dedicated work, and also to the many contributors to Weka from outside our group at Waikato.
Data retrieval, like datamining, extracts interesting data and information from archives and databases. The difference is that, unlike datamining, the criteria for extracting information are decided beforehand so they are exogenous from the extraction itself. A classic example is a request from the marketing department of a company to retrieve all the personal details of clients who have bought product A and product B at least once in that order. This request may be based on the idea that there is some connection between having bought A and B together at least once but without any empirical evidence. The names obtained from this exploration could then be the targets of the next publicity campaign. In this way the success percentage (i.e. the customers who will actually buy the products advertised compared to the total customers contacted) will deﬁnitely be much higher than otherwise. Once again, without a preliminary statistical analysis of the data, it is difﬁcult to predict the success percentage and it is impossible to establish whether having better information about the customers’ characteristics would give improved results with a smaller campaign effort.
The statistical analysis in this chapter was carried out on a data set kindly pro- vided by AC Nielsen, concerning transactions at a large supermarket in southern Italy. The data set is part of a larger database for 37 shop locations of a chain of supermarkets in Italy. In each shop the recorded transactions are all the trans- actions made by someone holding one of the chain’s loyalty cards. Each card carries a code that identifies features about the owner, including important per- sonal characteristics such as sex, date of birth, partner’s date of birth, number of children, profession and education. The card allows the analyst to follow the buying behaviour of its owner: how many times they go to the supermarket in a given period, what they buy, whether they follow the promotions, etc. Our aim here is to consider only data on products purchased, in order to investi- gate the associations between these products. Therefore we shall not consider the influence of demographic variables or the effect of promotions.
Much business intelligence can be found in this unstructured or semi structured data in variety of format only if it can be extracted efficiently on time. Veracity and value of the extracted information from different sources depends on how filtered the information is and how fast the information is extracted to be useful at the right time. As the data growth is exponential and at a very fast rate, human cannot process this data even after surfing the internet twenty four hours a day and seven days a week. On the contrary, computers are very good at processing huge amount of data quickly, only if humans have developed good methodologies and good algorithms for fast data processing. Traditional data processing involves structured data in relational database management system (RDBMS). Online transaction processing (OLTP) system in data bases and data ware houses are there for data collection and modification, by customers and administrators. Structured query language (SQL) is there to get any information stored in the database or to update the data. To solve the challenges of structured data processing in RDBMS such as redundancy avoidance, transaction management, concurrency control and database consistency management there are proven techniques. Further data analytics field on structured data is also very active and matured. There are four different kinds of analytics like descriptive, diagnostic, predictive and prescriptive that are performed to transform the raw data to information and then to knowledge. Based on that knowledge, finally strategic decisions are made by the, “C” level executives in a business as per their experience and wisdom.
A project has been deﬁned as any piece of work that is undertaken or attempted. Consequently, project management involves “the application of knowledge, skills, tools and techniques to a broad range of activities to meet the requirements of the particular project” . Project management is needed to organize the process of development and to produce a project plan. The way the process is going to be developed (life cycle) and how it will be split into phases and tasks (process model), will be established. This project deﬁn- ition  exactly describes the common understanding, its extent and nature, among the key people involved in a project. Thus, any datamining project need to be deﬁned to state the parties, goals, data and human resources, tasks, schedules, expected results, that comply the foundation upon which a successful project will be built. In general, any engineering project iterates through the following stages between inception and implementation: Justiﬁ- cation and motivation of the project, Planning and Business Analysis, Design, Construction, Deployment. In fact in software engineering this approach has been successfully applied. Although a datamining project has components similar to those found in IT, the nature is diﬀerent even some that concepts need to be modiﬁed in order to be integrated.