and the classification issue that lacking of prior knowledge. SVM is used in systems for face recognition [3, 4], road sign recognition and other similar application areas  because of its sound theoretical foundation and good oversimplification ability in practical application. SVM works well in both linear and non-linear conditions, and finding the optimal unraveling hyper plane is the key to separate data. For non-linear circumstances, SVM exploits the kernel trick to map low-dimension data into high dimension feature space. In the practical application, SVM makes use of all the labeled data to find the unraveling rule, but training on large scale data can bring with higher computation cost. In order to decrease the computational complexity, the solution that can be exploited includes two species, one is to improve the algorithm itself, such as the Least Square SVM [6, 7], the SMO  (Sequential Minimal Optimization) under semi positive definite kernel; the other is to reduction the number of input vectors. The main task of clustering  is to group the objects into clusters, objects in the same cluster are more semblable than those in dissimilar clusters. It can find the relationships amongst data objects in an unsupervised way. A lot of clustering algorithms have been proposed and better, aiming to enhance the efficiency and accuracy. According to cluster mode, clustering algorithms can be categorized into: centroid-based bunching, hierarchical clustering, distribution-based clustering and density based clustering.
Web usage mining is an innovative approach to automatically identify the user interaction patterns from web services and measures user behaviour, when the user works on the web. It helps to identify type of contents in which user are more interested. Today various business firms and e-commerce societies are follows these rules for evaluating life time value of client and gives better link according their browsing behaviours. Web usage mining retrieves desire knowledge from server log, proxy log, browser log and managed databases. Web server log contains the history of page log and proxy server executes between customer browser and web server. It works on three forms such as data pre-processing, pattern discovery and pattern evolution.
There are some software programs like robots, spiders or crawlers which are used to examine the documents in the web, this job will be done by search engines. A robot is a piece of software that automatically follows hyperlinks from one document to the next around the Web. A robot sends information back to its main site when it discovers a new site and it will be indexed. These robots are also used to update previously catalogued sites. Spiders and bots are software programs used by search engines which are used to survey the Webs and help to build their databases. These programs retrieve web documents and it was analysed. Search engine index is built by the data collected from the web pages. The query is searched from a search engine site, by searching the index of search engine of all analysed web pages. The best URLs are then returned as hits and ranked in order with the best results at the top .
Available Online at www.ijpret.com 1421 real world it is a software engineering model. The designed Goals of SemanticData system is to represent the real world as accurately as possible within some data set. There is linear and hierarchical organization of data to give certain meanings like in below example. Semanticdata allow the real world within data sets by representing, machines to interact with worldly information without human interpretation .This semanticdata is organized on binary models of objects, mostly in groups of three parts consisting of two objects and their relationship. Consider example, if one wanted to represent a pen is on a letter book, the organization of data might look like: PEN LETTER BOOK. The objects (pen and letter book) are interpreted with regard to their relationship (residing on). The data is organized linearly, telling the software that as PEN comes first in the line, it is the object that acts. i.e., the position of the word makes the software to understand that the pen is on the letter book and not that the letter book is sitting on the pen. Databases designed in this concept have greater applicability and are easily integrated into other databases.
ARl aka dependency modeling searches for relationships between variables. For example, a gym might gather data on customer eating habits. Using ARl, the gym can determine which products are frequently bought together and use this information for advertising purposes. The existence of brain lesions in MR images may suggest other types of lesions having a clear spatial relation to other structures. To count the occurrences of such a pattern hints at the diagnosis. Methods on ARl appear in 6, 11 .
As figure 1 describes at the bottom is XML that is suitable for sending documents across web. XML allows writing structured web documents with user-defined vocabulary. RDF is a basic data model, similar to entity-relationship model, to write simple statements about Web objects (resources). The RDF data model does not rely on XML, but RDF has an XML- based syntax. RDF Schema provides modeling primitives for organizing Web objects into hierarchies. Key primitives of web objects are classes, properties, subclass, sub-property relationships, and domain restrictions. It can be viewed as a primitive language for writing ontologies. The Proof layer involves the actual deductive process as well as the representation of proofs in Web languages (from lower levels) and proof validation. The Logic layer is used to enhance the ontology language and to allow the writing of application-specific declarative knowledge. Finally, the Trust layer will emerge through the use of digital signatures and other kinds of knowledge, based on recommendations by trusted clients .
Datamining techniques can follow three different learning techniques i.e., supervised, unsupervised and semi-supervised . In supervised learning, the technique works with a set of examples whose labels are known. The labels can be nominal values in the case of the classification task, or numerical values in the case of the regression task. In unsupervised learning, in contrast, the labels of the examples in the dataset are unknown, and the technique typically aims at grouping examples according to the similarity of their attribute values, characterizing a classifying task. Finally, semi-supervised learning is usually used when a small subset of labeled examples is available, together with a large number of unlabeled example. Various datamining techniques used for classifying proposed in this paper are explained in the following section.
In recent years, large amount of informative websites, web pages and web documents are popular as huge collection. Any popular search engine returns thousands of related links to a search query. But it has become difficult for users to get the most relevant information from the related information efficiently. Modeling and analyzing web navigation behavior is helpful in understanding what information user’s online demands. It can be used for different purposes such as personalization, recommendation system improvement and site. Web usage mining is concerned with finding user navigational patterns on the World Wide Web by extracting knowledge from web logs.
It can be considered that the potentially novel associations are likely to be found among those with a small enough number of symbols, and a large enough number of genesets. Thus the lists can be screened over numerical criteria. An example of screening (number of symbols smaller than 10, number of genesets larger than 2) appears on columns 4 to 6 of Table 2. After numerical screening, the remaining associations were tested in STRING 9.0 . STRING distinguishes evidence of association according to neighborhood, gene fusion, cooccurrence, coexpression, experiments, databases, textmining, homology. We considered that two symbols were connected in STRING if at least one of the 8 links exists, i.e. if there exists at least one edge in the evidence view. The results only reflect the status at the date when comparisons were made. STRING is in constant evolution, and includes new interactions almost daily. Several of the groups found disconnected when the comparison was made, may have been connected since. Among the associations detected in the full database C2 at threshold h = 10 −15 , nearly all fell under informational and/or biological redundancy. Very few disconnected STRING graphs were detected in that experiment; examples include: PRLHR, DRD5 found together in 12 genesets; UQCRC1, SDHA found together in 23 genesets; ZNF367, UHRF1 found in 24 genesets. In C2K and C2Cancer, a majority of detected associations also corresponded to STRING-connected graphs. In the other three (more specific) selections, a majority corresponded to disconnected, or even empty graphs. Here are two examples of STRING-disconnected associations from C2Breast (many more can be found in the corresponding additional file): ERBB3, MYB found together in 7 genesets; DSC3, KRT14, PDZK1IP1 found together in 6 genesets. Once again, algorithmic detection cannot be considered a proof that ERBB3 (v-erb-2 erythroblastic leukemia viral oncogene homololog 3) and MYB (v-myb myeloblastosis viral oncogene homolog) are functionally related, even though it has been shown that both genes are deregulated by mutations of the transcription factor TWIST in human gastric cancer .
to recover and value important and helpful details. Second, scientists will use the accessible universal resource locator, time, information science modify, and web content details to make a four- dimensional read on the net log knowledge supply and execute a four-dimensional OLAP analysis to seek out the highest customers, prime used websites, most often used times, and so on. These results can facilitate verify customers, marketplaces, and different organizations. Third, exploring blog records will expose organization designs, consecutive designs, and internet accessibility designs. Internet accessibility routine exploration typically needs taking more measures to get a lot of individual traversal data. This data, which might embrace individual browsing series from the net server’s barrier pages beside connected data, permits elaborated blog analysis. Researchers have used these blog data to assess program performance, enhance program vogue through internet caching and page pre-fetching and dynamical, verify the characteristics of internet traffic, and to assess individual answer website vogue. As an example, some studies have instructed versatile internet sites that enhance themselves by learning from individual access designs. Blog analysis can even facilitate develop personalized internet services for individual customers. Since blog data provides details regarding explicit pages’ name and therefore the techniques wont to access them, these details may be incorporated with web page and linkage framework exploration to assist position Websites, categories web records, and develop a multi layered web details platform.
Prototype-based partitional clustering algorithms can be divided into two classes: crisp clustering where each data point belongs to only one cluster, and fuzzy clustering where every data point belongs to every cluster to a certain degree -. Fuzzy clustering algorithms can deal with overlapping cluster boundaries -. Partitional algorithms are dynamic, and points can move from one cluster to another. They can incorporate knowledge regarding the shape or size of clusters by using appropriate prototypes and distance measures. Most partitional approaches utilize the alternating optimization techniques, whose iterative nature makes them sensitive to initialization and susceptible to local minima. Two other major drawbacks of the partitional approach are the difficulty in determining the number of clusters, and the sensitivity to noise and outliers .
As noted earlier, the primary motivation behind the use of clustering (and more generally, model-based algorithms) in collaborative filtering and Web usage mining is to improve the efficiency and scalability of the real-time personalization tasks. For example, both user-based clustering and item-based clustering have been used as an in- tegrated part of a Web personalization framework based on Web usage mining [88, 82]. Motivated by reducing the sparseness of the rating matrix, O’Connor and Herlocker proposed the use of item clustering as a means for reducing the dimensionality of the rating matrix . Column vectors from the ratings matrix were clustered based on their similarity, measured using Pearson’s correlation coefficient, in user ratings. The clustering resulted in the partitioning of the universe of items and each partition was treated as a separate, smaller ratings matrix. Predictions were then made by using tradi- tional collaborative filtering algorithms independently on each of the ratings matrices. While some statistical methods such as sampling, as well as clustering, can mitigate the online computational complexity of collaborative filtering, these methods often re- sult in reduced recommendation quality . However, in the context of Web usage mining it has been shown that proper preprocessing of the usage data can help the clus- tering approach achieve prediction accuracy in par with standard k-nearest-neighbor approach .
In the third chapter the different processes that were used for creating the data table of the Tell es-Safi vessels are described. To start with, all the drawings of the vessels were scanned, and the images were processed in a way that enabled importation into MATLAB software. The Karasik and Smilansky programs were then run on this data. Following that, using Microsoft ACCESS, a special form was created for entering all the data relating to measurements that were directly and manually taken from the vessels. In the first phase the vessel drawing was incorporated within the form, and attributes that were seen in the drawing were recorded. Some of these attributes were defined typologically, such as the form of the rim or of the base. However, other attributes were easier to measure, such as rim direction or number of handles. A secondary form was used to record decoration attributes. In the next phase, attributes measured from the actual vessels were recorded. Such attributes were: hardness, color, slip preservation, erosion level, etc. In addition, some basic morphological attributes were measured, since some of the whole vessels had not been drawn. The next phase consisted of recording traits measured by macroscopic analysis of the clay fabric. A small shard sample was taken from most of the vessels for this analysis. By examining a freshly broken section with a magnifying glass, various attributes were measured, such as: types of inclusions, their frequency, size, shape, color and luster, or voids types, and their frequency, shape and size. Additional attributes were measured by other means, such as, differences in color between core and margins, core hardness, reaction to hydrochloric acid, reaction to a magnet, specific weight, etc.
Semantic change detection (i.e., identify- ing words whose meaning has changed over time) started emerging as a grow- ing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social sci- ence. However, several obstacles make progress in the domain slow and diffi- cult. These pertain primarily to the lack of well-established gold standard datasets, resources to study the problem at a fine- grained temporal resolution, and quantita- tive evaluation approaches. In this work, we aim to mitigate these issues by (a) re- leasing a new labelled dataset of more than 47K word vectors trained on the UK Web Archive over a short time-frame (2000- 2013); (b) proposing a variant of Pro- crustes alignment to detect words that have undergone semantic shift; and (c) intro- ducing a rank-based approach for evalu- ation purposes. Through extensive nu- merical experiments and validation, we il- lustrate the effectiveness of our approach against competitive baselines. Finally, we also make our resources publicly available to further enable research in the domain. 1 Introduction
no relation. To evaluate the accuracy of the sys- tem, we randomly sampled 100 of these verb pairs, and presented the classifications to two human judges. The adjudicators were asked to judge whether or not the system classification was ac- ceptable (i.e. whether or not the relations output by the system were correct). Since the semantic rela- tions are not disjoint (e.g. mop is both stronger than and similar to sweep), multiple relations may be appropriately acceptable for a given verb pair. The judges were also asked to identify their pre- ferred semantic relations (i.e. those relations which seem most plausible). Table 3 shows five randomly selected pairs along with the judges’ responses. The Appendix shows sample relationships discov- ered by the system.
for information extraction from such large datasets. In this work, we propose a multidimensional indexing method, based on a static R-tree data structure, to eﬃciently query and mine large astrophysical datasets. We follow a top-down construction method, called VAMSplit, which recursively splits the dataset on a near median element along the dimension with maximum variance. The obtained index partitions the dataset into nonoverlapping bounding boxes, with volumes proportional to the local data density. Finally, we show an application of this method for the detection of point sources from a gamma-ray photon list.
ABSTRACT: A cloud storage system consists of a bunch of storage servers over the online. The foremost aim is to supply secure storage services over cloud storage system. There are several completely different techniques were exist for storage services, whereas providing Associate in Nursing data confidentiality solutions for the knowledge as a service paradigm unit of measurement still in operation and is not completed still. Placing essential knowledge within the hands of a cloud supplier ought to associate with the guarantee of security and convenience for storing data. We tend to propose a completely unique design that integrates cloud info services with knowledge confidentiality and the chance of capital punishment synchronic operations on encrypted data. This is often the primary answer at the bottom of geologically distributed customers to attach on to Associate in Nursing encrypted cloud info, and to execute synchronic and irregular operations collectively with those modifying the info structure. The projected design has the more improvement of eliminating midway proxies that bound the elasticity, availability, and scalability properties that area unit intrinsic in cloud-based solutions. The effectuality of the projected design is evaluated all the way through notional analyses and in depth experimental results supported a image achievement subject to the TPC-C regular benchmark for a range of numbers of clients and network latencies.
Second experimental setup. In the second set of experiments, we exploited the corre- lations discovered in the first experimental setup. We used the scores of background knowledge terms to prune the background knowledge. Specifically, we ran the NetSDM algorithm by setting the shrinkage coefficient values of c to values between 0.005 and 1, thereby running the SDM algorithms on the gene ontology (GO) background knowl- edge containing only a subset of as little as 0.5% of the terms with the highest scores. We calculated the P-PR values of GO terms in two ways: (i) we viewed is-a relations as directed edges pointing from a more specific GO term to a more general term, and (ii) we viewed the relations as undirected edges. When running the experiments with Hedwig, we set the beam size, depth and minimum coverage parameters to the values that returned the best rules on each data set in the first set of experiments. For example, the results of the first experimental setup showed that the rules obtained by setting the beam size or rule depth to one are too general to be of any biological interest; we therefore decided to set both values to 10. In the case of rule depth, this value allows Hedwig to construct arbitrarily long rules (given that in our data sets Hedwig returned no rules of length greater than five). Setting the beam size to 10 allows Hedwig to discover important rules (as shown in the first set of experiments) in a reasonable amount of time; note that the run time of Hedwig increases drastically with increased beam size, and at size 10 the algorithm takes several hours to com- plete. Running the experiments with the Aleph algorithm, we again used the setting recommended by the algorithm author.
SEGS was the first special purpose semantic subgroup dis- covery algorithm developed. Recently, we developed two new general purpose semantic subgroup discovery systems: SDM-SEGS and SDM-Aleph . SDM-SEGS is based on SEGS and can be used to discover subgroup descrip- tions from ranked data as well as from labeled data with the use of background knowledge in form of OWL ontologies. SDM-Aleph is based on the ILP system Aleph. 1 It was designed to be used in a similar way as SDM-SEGS. Unlike SDM-SEGS which is limited to four ontologies as input and only one additional interacts relationship, in SDM- Aleph any number of ontologies and additional relations between the input examples can be specified, which is due to the powerful underlying first-order logic formalism of the ILP system Aleph. SDM-SEGS and SDM-Aleph are implemented within a new semanticdatamining toolkit, named SDM-Toolkit . SDM-Toolkit has been made publicly available within the Orange4WS service-oriented datamining environment . In , we illustrate the use of SDM-Toolkit tools for biomedical workflow con- struction and their execution in Orange4WS on the same two biomedical problem domains, ALL and hMSC, which were used in the evaluation of the utility of SegMine . A qualitative evaluation of SDM-SEGS and SDM-Aleph, supported by experimental results and comparisons with SEGS, showed that SEGS and SDM-SEGS are more appro- priate for data analysis in biomedical domains where rule specificity is desired, while SDM-Aleph is a more general purpose system, resulting in more general rules of lower precision.