The Textmining and Datamining supports different kinds of algorithms for classification of large data sets. The Text Categorization is traditionally done by using the Term Frequency and Inverse Document Frequency. This method does not satisfy elimination of unimportant words in the document. For reducing the error classifying of documents in wrong category, efficient classification algorithms are needed. Support Vector Machines (SVM) is used based on the large margin data sets for classification algorithms that give good generalization, compactness and performance. Support Vector Machines (SVM) provides low accuracy and to solve large data sets, it typically needs large number of support vectors. We introduce a new learning algorithm, which is comfortable to solve the dual problem, by adding the support vectors incrementally. It majorly involves a classification algorithm by solving the primal problem instead of the dual problem. By using this, we are able to reduce the resultant classifier complexity by comparing with the existing works. Experimental results done and produce comparable classification accuracy with existing works.
the course of the study, informed by what we discover from analysis of the controlled experiment. Ground truth is es- tablished by clinical cognitive assessments of each partici- pant at the start, mid-point and end of the study period. Our current analysis strategy is to apply datamining clus- ter and pattern analysis algorithms to investigate changes within individuals over time and inter-individual variations with known norms for age/gender cohorts of our senior par- ticipants (range 65–78 years). Given these reassuring find- ings, future work includes sequence analysis such as learn- ing Markov models or using SPADE-like algorithms, which have been applied to finding temporal patterns in web-log data (Demiriz, 2002), to discover richer interaction of low- level events over time capable of identifying signs of MCI. Sequence mining will be used to identify atypical user be- haviour and errors which might indicate cognitive problems linked to MCI and early dementia. Integration of evidence from datamining activity patterns, sequences of computer operation, and text analysis metrics will be investigated us- ing Bayesian nets to implement a ‘diagnostic’ model that traces measures derived from data and textmining to cog- nitive indicators which are associated with MCI. The chal- lenge we face is finding a weak signal indicative of disease in noisy data where variations might be caused by interrup- tions, changes in user mood, or many environment factors.
The above mentioned three algorithms are used for mining process in the text. These algorithms are used for classifying the computer files based on their extension. For example, .docx, .pdf, .xls, .ppt, and so. The performance of Meta algorithms are analyzed by applying the performance factor such as classification accuracy and error rate. The current research in the area of textmining tackles the problems like text representation, classification, clustering or searching the hidden patterns. It is used to describe the application of datamining techniques to automated discovery of useful or interesting knowledge from unstructured or semi-structured text. The procedure of synthesizing the information by analysing the relations, the patterns, and the procedures among textual data semi-structured or unstructured text.
Textmining is also called textdatamining and it is defined as finding previously unknown and potentially useful from textual data, textual data may be either semi structured or unstructured. Textmining is used to extract interesting information or knowledge or pattern from the unstructured texts that are from different sources. It converts the words and phrases in unstructured information into numerical values which may be linked with structured information in database and analyzed with ancient datamining techniques. There are many techniques used in textmining such as information extraction, information retrieval, natural language processing (NLP), query processing, and categorization and clustering.
―Textmining is the study and practice of extracting information from text using the principles of computational linguistics‖. TextMining is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. A key-notion point is the linking together of the extracted information together to form new facts or new hypotheses to be explored further by more conventional means of experimentation. Textmining or knowledge discovery in text (KDT) — for the first time mentioned in Feldman et al. [FD95] — deals with the machine supported analysis of text. It uses techniques from information retrieval, information extraction as well as natural language processing (NLP) and connects them with the algorithms and methods of KDD, datamining, machine learning and statistics.
Gene expression data is experimental data which can be used to check whether a gene has indeed been upregulated or downregulated with respect to a dis- ease. This methodology compares to what level genes were expressed in cancerous cells versus healthy cells. It is unaffordable and infeasible to try wet-lab analysis of such a huge set of genes. Therefore, machine learn- ing and datamining techniques (including frequent pat- tern mining, clustering and classification) can be used to lower this number of genes down to a manageable set of genes which are anticipated to be statistically linked with the disease. This way, biologists will concentrate only on the identified small set as potential cancer bio- markers instead of unrealistic case of testing every gene in the wet-lab as potential cancer biomarker. In other words, datamining techniques can save the time and cost of cancer researchers, turning their research goals into something potentially achievable. This is illustrated by the test results reported in this paper.
Yanjun Li, D. Frank Hsu, and S. M. Chung, “Combining Multiple Feature Selection Methods for Text Categorization by Using Rank-Score Characteristics”, Proc. of the 21 st IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2009, IEEE CS Press, 2009, pp. 508 – 517.
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 document clustering 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 text clustering. 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.
Given this increased amount of available data and metadata, machine learning processes, supported by integration with digital library systems, are able to be more accurately trained, reducing manual labor and adding value to previous investment in content creation. Datamining processes typically include classification (predict if a given document is a member of a particular class or domain), clustering (grouping together similar documents) and association rule mining (discovering rules that interrelate documents or terms within those documents). However, both data and textmining processes are computationally expensive and can benefit in turn from distribution across multiple machines for parallel computation.
Abstract -Textmining or knowledge discovery is that sub process of datamining, which is widely being used to discover hidden patterns and significant information from the huge amount of unstructured data. The enormous amount of information stored in unstructured / semi structured data cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific pre-processing methods and algorithms are required in order to extract useful patterns. In this study, we compared the performance of these classifications by applying the method of Bayesian methods, k-NN, decision trees, SVM, and as a neural network in classification on famous 20_newsgroup dataset from CMU Text Learning Group Data Archives, which has a collection of 20,000 messages, collected from 20 different net news newsgroups. The news will be classified according to their contents.
This is the oldest and most difficult problems in the field of artificial intelligence. It is the analysis of human language so that the computers can understand natural languages as humans. As this goal is still some way off, NLP can do some types of analysis with a high degree of success. Shallow parsers recognize only the major grammatical elements in a sentence, such as noun phrases and verb phrases, whereas deep parsers generate a whole representation of the grammatical structure of a sentence. The role of NLP in textmining is to present the systems in the information extraction phase with linguistic data which they need to perform their task. As this is done by annotating documents with information like sentence boundaries, part-of-speech tags, parsing results, which can then be read by the information extraction tools.
ABSTRACT: Datamining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in datamining that work to provide an established, proven structure from a voluminous collection of facts. In this paper, our focus is to analyze clusters of documents obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the documents. Cluster is a group of objects that belongs to the same class. In other words, similar objects are grouped in one cluster and dissimilar objects are grouped in another cluster using the k-means algorithm. Classification is a task of assigning instances to predefined classes. We have a Training set containing data that have been previously categorized, and based on this Training set the algorithms finds the category that the new data points belongs to it using the secure hashing algorithm. K-means algorithm is used for classification and SHA-256 algorithm is used for protect the data securely in digital hash code.
Textmining can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. Datamining or Textmining plays a important role in decision making because through these mining techniques we can analyse the data and on the basis of result we can take a decision. Now a days social media sites like twitter are widely used to share user options on various topics, twitter gives a platform to user to share their views and thoughts on various field like political, industrial, education and there is a petabytes of data generated by twiiter in a day.
Text analytics  is concerned with uncovering unstructured data and providing it a form suitable to elicit meaningful content, in other words, knowledge discovery from a large volume of data. The techniques improve that conventional Extract, Load and Transform (ETL) pipeline of datamining. For efficient exploration of knowledge contained in every datatext analytics are inevitable. They form a bridge between data and all other applications that run on top of data. Manually analyzing huge volumes of data to find hidden knowledge is a tedious work and also impossible. It also helps to extract specific points and entities of interest thereby providing a personalized textmining process. The rate of data accumulation in the form of unstructured data is on an increasing trend for the past few decades. Data is in fact exploding with more and more dimensions in an exponential way. The tools that could handle such enormously growing data whether it is in structured and unstructured form is a valuable asset, as data is the new oil, datamining becomes the force that controls the data. Textmining has its applications in almost every field wherever data is generated. Some of the most famous applications of textmining are, healthcare analytics, recommender systems, social media analytics, educational datamining, government information mining, security, law etc. Pattern mining  is the procedure to mine effective trends present in the data. It is one of the techniques in datamining concerned with extracting most related entities from the object of analysis. The data model that is used to derive patterns of interest is called pattern taxonomy model. The patterns and their inherent relationships are exhibited in hierarchical manner. Knowledge discovery can be further enhanced if pattern taxonomy model is used to update the extracted patterns from text documents.
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, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
Several past studies proved the helpfulness of datamining and machine learning approaches for job placement. For instance, in (Min and Emam, 2003), rules created by a decision tree are used to manage the recruitment of truck drivers. For this study, the authors use empirical data derived from a survey submitted to several transport companies. (Buckley et al., 2004) present an automated recruitment and screening system, showing conservative savings of 6 to 1 ratio, due to reduced employee turnover, reduced staffing costs and increased hiring process efficiency. In (Chi, 1999), the authors apply principal component analysis to establish jobs that can be adequately performed by various types of disabled workers. A dataset of 1259 occupations summarized in 112 different jobs is used; 41 available skills are analyzed with principal component analysis, finding five principal factors (occupational hazard, verbal communication education and training, visual acuity, body agility, and manual ability). Finally, the 112 job titles are classified into 15 homogeneous clusters, creating useful data to expand both the counselor and counselee perspectives about job possibilities and job requirements through the five principal factors.
Information that is gathered have to be converted into the form which is to be understandable by the software or language we are using. For that purpose, we are performing several tasks like removal of unwanted texts, symbols, as well as words which are generally not useful for performing textmining .Since text files contains unwanted and inconsistent data, initially there is a need to perform cleaning procedure. Pre-processing and extracting will be done with the help of program code which helps in removing punctuation marks, stop words and some particular information which is not required for further checking process .For this Data Pre- Processing we had used two algorithms which were used to help for cleaning the data .The main algorithms are: 3.4.1 Porter Stemming algorithm:
In the last few years, most of the research on sentiment classification focuses on how to improve the classification accuracy of the entire text, but rarely analyzes the sentiment polarity based on the aspects or targets appearing in the text. In tourism analysis, not only is knowing the overall sentiment tendency of the tourists’ comments needed, but also knowing the various sentiments of each entity in the tourism or each aspect of the tourist comments is required, so as to better self-evaluate and propose more targeted solutions. Due to the complexity of the process and the lack of related corpora, most of the works are unable to achieve an effective evaluation of aspect extraction and sentiment classification. The research  considered the possibility of describing the topic words by considering the distance between the topic words and the sentiment words, and exploring the preferences of tourists for tourism products. This method is simple, but the accuracy of the result is low due to the existence of the virtual target and the implicit evaluation object . The study  extracted topics from the destination reviews based on LDA and then analyzed the sentiment state of each topic in more detail or for finer gain. The study  used textmining and sentiment analysis techniques to analyze hotel online reviews to explore the characteristics of hotel products that visitors were more concerned about. Most of these methods only make use of the model, while the adaptability of the model to the domain is not well explained.