clustering information entities based on statistical methods в наличии / купить интернет-магазине

The World Wide Web has grown amazingly in the last two decades. Abundant information is available on the web and people regularly search the web to gather relevant information. Search engines play an important role in making users search the contents online using textual queries and other features to suite their requirements. Search engines now provide various added services too. This work presents critical evaluation of five popular search engines i.e. Google, Yahoo, AltaVista, Ask and Bing based on added features along with end-user experiences recorded during a survey. Images of thirty ambiguous words are retrieved from these search engines and analyzed and compared on the basis of their features, behaviour, functionalities, time taken, number of results and their versatility. The outcome of this study indicates the search engine(s) that provide good image results for ambiguous queries and the ones that provide the most features. We also put these search engines to various statistical comparisons including statistical tests to prove the hypothesis considered for this study. The results are elaborated using graphical interpretations.

Trading strategy is very important in today''s financial market. It will be useful to understand the trend of trading market in advance. The optimization algorithms implemented with the technical indicators shows significant improvement in the prediction of the trading strategy signals as compared to the traditional methods. Moreover, flexibility of the optimization algorithms to integrate with various technical indicators gives way to improve the proposed trading strategy model.

This book presents various algorithms to compute semantic similarities between english texts. I explored three different algorithms for computing English sentence similarity. The first algorithm, which is well-explored in the literature [Salton and Buckley, 1988, Wu and Salton, 1981], weights words in each sentence according to term frequency and inverse document frequency (tf-idf ) and uses no semantic information. The second algorithm uses measures of the semantic distance between words belonging to the same part of speech. The third algorithm combines the tf-idf scores and the semantic distance scores between words. I evaluated the performance of the second and third algorithms on two data sets: O’Shea’s set of sentence pairs with human similarity judgements [Li et al., Aug, Rubenstein and Goodenough, 1965], and Microsoft Research’s sentence-level paraphrase dataset [Rus et al., 2012]. On O’Shea’s data set, the third algorithm more accurately matches human judgments than the second. On the Microsoft data set, there was not a significant difference between the two algorithms

Clustering is an important tool for many applications such as document clustering, gene expression analysis, etc. In many such cases, the data can be represented as a set of instances expressed by their attributes, in the form of a matrix. Clustering instances, such as documents, depends on their attributes (words) and vice versa, thus forming a dual relationship between instances and their attributes. Co-Similarity Approach to Co-Clustering emphasizes on a technique that exploits the dual nature between instances and their attributes to find similarities between objects in each set. It provides and analyzes results of applying this technique on two different domains- Document clustering and Gene Expression Analysis.

The increasingly progress in computer science and information technology allowed to deal further with complex systems and in closer respect with real-world application. This, in turn, has led to suggestion of software agents, which has been, thereafter, widely used. With the increasing interest on software agents, the "Automated Negotiation" has become a very popular topic of research to facilitate negotiation process design and enhance over all outcomes. "Automated Negotiation" could be defined as a complex process by which two or more agents communicate and attempt to achieve a mutually acceptable agreement on some specific matter. Besides developing a bilateral "Automated Negotiation" model between agents within an incomplete information state, this book provides an architecture of such agents (“buyer” or “seller”). To do so, a new offer generation approach of three adaptive families of tactics has been proposed. Finally, with regard to “Big five” personality model and by introducing the cognitive orientations, we have developed a negotiator agent’s architecture taking into consideration the effects of the negotiator agent's personality on negotiation process and outcome.

Yes, YOU! Do you know that you have already encountered the concepts of data mining in your everyday life? HOW? Let's see. Data Mining: from these words you can understand what it is. It is the extraction of information from data. Clustering is a part of data mining. It is a mechanism that groups similar data in one group and dissimilar data in other groups. Believe me, you have already encountered the mechanism of clustering whenever you have entered the library. Got shocked? Let me explain. Suppose you like novels, so all the books of novel are stored in the same side of shelves. Suppose, from the novels you like the most of Shakespeare. So, furthermore, all the novels of Shakespeare are grouped in one shelf. So, here first we group ,that is, cluster the similar books, for example ,novels and then furthermore group the novels , i.e cluster novels based on the name of the authors. So, that is how it goes.In order to do so, we use different algorithms. The approach in this book is to use the algorithm that will group these books in a fast and accurate manner. Reading this, try to create a more efficient method. Say no to the problems! PROBLEM is the biggest problem in the world.

In this book, DNA- based cryptography methods are proposed as combined with data hiding techniques for an increased level of security using basic concepts and properties of the Deoxyribonucleic Acid (DNA). These methods are implemented mainly on two stages: the first encrypts the plaintext using Amino acid and DNA based Playfair cipher. The second applies a secure insertion, complementary or substitution methods to hide the encrypted DNA ciphertext into some reference DNA sequence. The proposed techniques are Playfair-insertion, Playfair-complementary and Playfair-substitution. Finally, Playfair-insertion-substitution method is introduced to achieve extremely data security.

One of the popular tools in the exploratory phase of data mining and pattern recognition is the Kohonen Self Organizing Map (SOM). Recently, experiments have shown that to find the ambiguities involved in cluster analysis, it is not necessary to consider crisp boundaries in clustering operations. In this Book, the Incremental Leader algorithm for the thresholding of the SOM (Inc-SOM) is proposed to validate the potential of a crisp clustering algorithm. However, the performance deteriorates when there is overlap between clusters. To overcome the ambiguities in the results of cluster analysis, a rough thresholding for the SOM (Rough-SOM) is proposed. In Rough-SOM, the data is first trained by a SOM neural network, then the rough thresholding, which is a rough set based clustering approach, is applied on the neurons of the SOM. The optimal number of clusters can be found by rough set theory, which groups the neurons into a set of overlapping clusters. An optimization technique is applied during the last stage to assign the overlapped data to the true clusters.

Content-based video analysis serves as the cornerstone for many applications: video understanding or summarization, multimedia information retrieval and data mining, etc. In our research, we aim to automatically detect anomalous events from surveillance videos (such as video monitoring traffic flow or pedestrian congestion in public spaces). Conceptually, what constitutes an anomaly varies in different video scenarios and is difficult to be defined in a general case. Our first solution is based on unsupervised clustering of object trajectories and anomalous trajectory identification in a probabilistic framework. Then we extend this solution to an arbitrary time length (any part of a complete trajectory) and multiple objects (multiple trajectories). Furthermore, we solve problems specifically in video scenarios where object trajectories cannot be extracted (e.g., crowd motion analysis). Our contributions include a novel hierarchical clustering algorithm and categorization of anomalous video events by spatiotemporal context.

The clustering problem has been addressed in many contexts and by researchers in many disciplines. This reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. Clustering is a challenging field of research in which its potential applications pose their own special requirements. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities have made the transfer of useful generic concepts and methodologies slow to occur. This book, therefore, provides a new methods for clustering complex data sets and improve performance of some algorithms that illustrated in the literature. Experimental results are shown in this book to demonstrate the effectiveness of the proposed algorithms.

This modular course on information and internet skills is a step by step procedure of acquiring information skills to research on the World Wide Web. When used in education, it becomes a study tool in the teaching of Information and Library skills for 1st year Students; a tool for preparing students (for research)in readiness for their long essay and a tool for preparing postgraduate students for research. Thus it fits in well as a good material for teaching information and library skills, research methods, Computer and internet skills as well as copyright skills. Part of its contents which teaches on "how to evaluate information from the internet" especially search engines makes it an invaluable tool in the teaching of information literacy while also providing assurance in selection of quality materials from search engines. This is not another book in the market, it is rather the solution to lack of information literacy skills of students.

Now-a-days, organizations are becoming more and more dependent on their information systems due to the availability of high technology environment. Information is also treated as vital like other important assets of an organization. Thus, we require Information Security Services (ISS) protocols to protect this commodity. In this thesis, investigations have been made to protect information by developing some ISS protocols. A key management protocol, which stores one-way hash of the password at the server, instead of storing plaintext version of password has been explained. A unique asymmetric cryptosystem for encrypting large messages has been described, which is not only efficient but also secure as compared to other asymmetric cryptosystems.Blind Signature Schemes (BSS) facilitate a requester to obtain signature from a signer on any document, in such a way that the signer can’t know anything about the message that is being signed. Four BSS have been described which are based on Elliptic Curve Discrete Logarithm Problem (ECDLP).A remote user authentication scheme based on ECDLP has been described.

This project designed to help performing sentences based search instead of matching keywords, results will be more accurate and similar to human comprehensive search result based.The project data based on Mysql database, written I Java programming language,based on number of IR libraries. The system spitted in two project (distributed System) as follow. The indexer project: which parsing the Mysql database to be processed by the Analyzer (tokenizing ,filtering Steaming) then to store into an index file.The Searcher project which apply the input keyword to the same Analyzer applied for the indexer, then to be matched with the indexed file, then to return the matched data into Web-Pages.The IR system can return accurate search result that could ordered by percentage of relevant,thanks to using the Steamer module, by apply systematic roles on each word, to return it to its infinitive (grammatically),a new Steamer module was developed to support Arabic language ,which is still a developing topic in the area of IR Systems the project implements a full object oriented design using some advanced design theories such MVC (Model View Control and DAO (Data Access Object), multi tier design.

Authors focus on the Knowledge engineering base techniques with particle implementation of CommonKADS methodology to design Knowledge base erecruitment portal. To reduce the organizational expenses for advertisement with traditional media and also to promote the eHR based recruitments system. Because such knowledge based portal works effectively, it efficiently deals in applicant''s information as compare to traditional methods, media using by human resource departments for the selection of competent candidates. The main purpose of this work is to develop understanding about the CommonKADS methodology, its implementation, and KE (knowledge engineering) procedures for knowledge based portal (KBP). Authors provide here complete practical guidelines and procedure for the acquisition of knowledge to facilitate the KM (Knowledge Management).

With the enormous growth of the digital information, it is necessary to find advanced ways to process it. The goal is to enhance information retrieval, information extraction and natural language processing. One of the most complicated processes is text mining which deals with finding high quality information from text. This book presents a statistical part-of-speech tagging model for Albanian. The training, testing and evaluation processes are done with Apache OpenNLP tool. The tagging process is performed based on a basic and a large tagset. The experiments are performed on a tagger model trained with corpus composed of a standard Albanian text written by Albanian authors. The tagger model is tested using a cross-validation and a sample text. Results showed that the accuracy of the trained tagger model in real testing environments was about 70%, and up to 98% when the environment settings were optimized for the best accuracy. It was also noticed that the overall accuracy for this model depends on the number of training tokens, level of grammatical and morphological complexity in text and special cases in language expressions.

Security is an important aspect in our daily life. Intrusion Detection Systems (IDS) are developed to be the defense against security threats. Current signature based IDS like firewalls and antiviruses, which rely on labeled training data, generally cannot detect novel attacks. The purpose of this study is to improve the performance of IDS in terms of detection accuracy and reduce False Alarm Rate (FAR). Clustering is an important task in data mining that is used in IDS applications to detect novel attacks. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-Means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The Gravitational Search Algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this study, a hybrid approach based on GSA and k-Means (GSA-kMeans), which uses the advantages of both algorithms, is presented.