Event Centric Modeling Approach in Colocation Pattern Snalysis from Spatial Data

Spatial co-location patterns are the subsets of Boolean spatial features whose instances are often located in close geographic proximity. Co-location rules can be identified by spatial statistics or data mining approaches. In data mining method, Association rule-based approaches can be used which are further divided into transaction-based approaches and distance-based approaches. Transaction-based approaches focus on defining transactions over space so that an Apriori algorithm can be used. The natural notion of transactions is absent in spatial data sets which are embedded in continuous geographic space. A new distance -based approach is developed to mine co-location patterns from spatial data by using the concept of proximity neighborhood. A new interest measure, a participation index, is used for spatial co-location patterns as it possesses an anti-monotone property. An algorithm to discover co-location patterns are designed which generates candidate locations and their table instances. Finally the co-location rules are generated to identify the patterns.Comment: 9 page

WEB PAGE ACCESS PREDICTION USING FUZZY CLUSTERING BY LOCAL APPROXIMATION MEMBERSHIPS (FLAME) ALGORITHM

ABSTRACT Web page prediction is a technique of web usage mining used to predict the next set of web pages that a user may visit based on the knowledge of previously visited web pages. The World Wide Web (WWW) is a popular and interactive medium for publishing the information. While browsing the web, users are visiting many unwanted pages instead of targeted page. The web usage mining techniques are used to solve that problem by analyzing the web usage patterns for a web site. Clustering is a data mining technique used to identify similar access patterns. If mining is done on those patterns, recommendation accuracy will be improved rather than mining dissimilar access patterns. The discovered patterns can be used for better web page access prediction. Here, two different clustering techniques, namely Fuzzy C-Means (FCM) clustering and FLAME clustering algorithms has been investigated to predict the webpage that will be accessed in the future based on the previous action of browsers behavior. The Performance of FLAME clustering algorithm was found to be better than that of fuzzy C-means, fuzzy K-means algorithms and fuzzy self-organizing maps (SOM). It also improves the user browsing time without compromising prediction accuracy

Assessment of optimum fish yields from reservoirs in Tamil Nadu

Time series data on catch and effort tor the early sixties to the mid eighties have been used tor the assessment at optimum yield tram tive reservoirs in Tamil Nadu by using Schaeter and Fox models. Fishing in respect at all these reservoirs has almost reached the optimum levels

Relation Between Clinical and Social Variables and Duration of Untreated Psychosis in First Episode Psychosis

Background: Several studies have shown that if untreated, psychosis can cause significant reduction in brain volume and worsening of disease outcome. Reducing Duration of Untreated Psychosis (DUP) is an important early intervention strategy. Factors associated with longer DUP remain poorly understood and there is a paucity of Indian studies in this regard. Methods: We compared sociodemographic data, premorbid functioning and psychopathology of patients of first episode psychosis with brief (six months or less) or long (more than six months) DUP. Premorbid Adjustment Scale and PANSS were used to assess the premorbid functioning and psychopathology respectively. Results: Lower age, higher educational status of the patient, acute onset, and higher scores for negative symptom or general psychopathology in PANSS were associated with brief DUP. Conclusions: Certain sociodemographic and clinical features are associated with brief DUP, and there is a need for more, especially longitudinal, studies on this topic.Â

Ability of Indian street rabies virus isolates to induce apoptosis by in-vitro

An attempt had been made in this study to know the ability of Indian street rabies virus isolates to induce apoptosis in murine neuroblastoma cells. Thirty Indian street virus isolates were tested by fluorescent antibody technique (FAT) among which 20 samples was positive. These positive samples were used to infect murine neuroblastoma (MNA) cells. Four street rabies virus isolates and challenge virus strain (CVS) were used in this study to show their ability to induce apoptosis. Total RNA was isolated and cDNA was synthesized by using gene specific primers pertaining to Caspase-1, BAD (Proapoptotic gene) and Glycoprotein gene of rabies virus. Challenge Virus Standard has been used as control in the amplification as well as in the demonstration of apoptosis. The expression of Caspase-1, BAD and glycoprotein genes in the murine neuroblastoma cells in comparision with CVS was measured by using densitometer. Street virus isolates expressed higher levels of Caspase-1 and BAD in comparison with CVS. Further TUNEL staining method showed higher apoptotic index when compared to CVS. This study is an initial attempt to exhibit the ability of Indian rabies street virus isolates to induce apoptosis in murine neuroblastoma cells (in vitro).</jats:p

Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering

Stream is endlessly inbound sequence of information, streamed information is unbounded and every information are often examined one time. Streamed information are often noisy and therefore the variety of clusters within the information and their applied mathematics properties will change over time, wherever random access to the information isn’t possible and storing all the arriving information is impractical. When applying data set processing techniques and specifically stream clustering Algorithms to real time information streams, limitation in execution time and memory have to be oblige to be thought-about carefully. The projected hymenopteran colony stream clustering Algorithmic is a clustering Algorithm which forms cluster according to density variation, in which clusters are separated by high density features from low density feature region with mounted movement of hymenopteran. Result shows that it created denser cluster than antecedently projected Algorithmic program. And with mounted movement of ants conjointly it decreases the loss of data points. And conjointly the changed radius formula of cluster is projected so as to increase performance of model to create it a lot of dynamic with continuous flow of information. And also we changed probability formula for pick up and drop to reduce oulier. Results from hymenopteran experiments conjointly showed that sorting is disbursed in 2 phases, a primary clustering episode followed by a spacing part. In this paper, we have also compared proposed Algorithm with particle swarm optimization and genetic optimization using DBSCAN and k -means clustering.</jats:p

Double Clustering Based Neural Feedback Method for Unstructured Text Data

Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional Neural Network for the purpose of feature extraction, so that the semantic meaning is also considered. Recurrent neural network (RNN) has the ability to study long-term dependencies prevailing in input whereas CNN models are for long known to be effective in feature extraction of local features of given input data. Unlike all the works previously carried out, this proposed work considers and carries out extraction of features and clustering of documents as one combined mechanism. Here result of clustering is send back to the model as feedback information thereby optimizing the parameters of the network model dynamically. Clustering in a double-clustering manner is implemented, which increases the time efficiency.</jats:p