CODS

Inter-organizational collaborations involve exchange of sensitive, utilitarian data. Such data exchanges require efficiently designed trust frameworks to explain data accesses in terms of the business logic of the collaboration, without becoming an ...

Recommender system (RS) is an important instrument in e-commerce, which provides personalized recommendations to individual user. Classical algorithms in recommender system mainly emphasize on recommendation accuracy in order to match individual user's ...

Vehicular sensor data consists of multiple time-series arising from a number of sensors. Using such multi-sensor data we would like to detect occurrences of specific events that vehicles encounter, e.g., corresponding to particular maneuvers that a ...

The dynamic time warping (DTW) distance is a popular similarity measure for comparing time series data. It has been successfully applied in many fields like speech recognition, data mining and information retrieval to automatically cope with time ...

Recently, conference publications have gained a wide popularity, specially in the domain of computer science. In conferences, the opportunity of personal interactions between the fellow researchers opens up a new dimension for the citation network ...

Video is an extremely effective way of reaching farmers with the latest agricultural technology and stories of other farmers. With a well-organised multifaceted video library, we can provide the farmers with services such as easy navigation, search and ...

Networks abstracted as graph lose some information related to the super-dyadic relation among the nodes. We find natural occurrence of hyperedges in co-authorship, co-citation, social networks, e-mail networks, weblog networks etc. Treating these ...

Online social media is all pervasive in this digitally connected world. It provides a great platform to share information and news, and have public discussions on these topics. These interactions happen on owned-sites as well as on earned social media. ...

Given India's night-time satellite images and census data, this paper proposes a method to correlate light intensity from images with state-wise poverty, population, GDP, and forest cover, and forecast future values of the same for each state. We use ...

In this paper, we propose a novel Multi-class Twin Support Vector Machine (MTWSVM) classifier on the basis of Direct Acyclic Graph (DAG) approach. MTWSVM is the multi-class extension of the recently proposed binary Twin Support Vector Machine (TWSVM) ...

Predicting future links or missing links is one of the useful application tasks in the analysis of social networks. Time and memory are major challenges for the link prediction task in large multi-relational social networks. This challenge is addressed ...

Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into product of two non-negative matrices, namely a signal matrix and a mixing matrix. NMF suffers from the scale and ordering ambiguities. Often, the source signals can be ...

While analyzing vehicular sensor data, we found that frequently occurring waveforms could serve as features for further analysis, such as rule mining, classification, and anomaly detection. The discovery of waveform patterns, also known as time-series ...

In most general learning problems, data is obtained from multiple sources. Hence, the features can be inherently partitioned into multiple views or feature sets. For example, a media clip can have both audio and video features. If we concatenate these ...

Influence maximization deals with finding a small set of target nodes that can be initially activated, such that the influence spread beginning with this causes maximum number of expected activated nodes in the network. Most of the existing algorithms ...

Establishing similarity between heterogeneous entities in a complex knowledge graph is a challenging task due to the unrestricted nature of categories and relation types. In large graphs, the semantic roles of relation types and entity categories are ...

This paper describes the design of three parallel algorithms for merging the topic hierarchies generated by a probabilistic topic model. These algorithms have been implemented on a shared memory multi-processor workstation and are primarily suitable to ...

In information retrieval, keyword-based queries often fail to capture actual information need, especially when the need is very specific and particular. Using natural language, however, a user can clearly tell what she wants (positive part) and what she ...

Collection of multiple annotations from the crowd workers is useful for diverse applications. In this paper, the problem of obtaining the final judgment from such crowd-based annotations has been addressed in an unsupervised way using a biclustering-...

Spatio-textual similarity join retrieves a set of pairs of objects which are close spatially and have similar textual contents. Due to the high cost of matching complex objects, most of the algorithms proposed for join run in two phases. In the first ...

Frequent pattern mining has been used in many applications of data mining. One of the reasons for the effectiveness of frequent pattern methods is that frequently occurring patterns can capture crucial aspects of the underlying semantics of the data. ...

The time complexity of Minimum Description Length based greedy agglomerative clustering algorithm is poor for a large data set. In this paper, we propose three different versions of GPU-based parallel algorithms, namely, time-efficient, memory-efficient ...

In this paper we have proposed an efficient algorithm QMINE to find closed frequent itemsets over a data stream. Our approach performs a few operations. Experiments have shown that our approach outperforms the previous approaches, significantly.

In this paper, we propose a fuzzy version of GDBSCAN called generalized fuzzy density based clustering algorithm (GFDBSCAN) that can be used to cluster people around key socio-economic parameters. GFDBSCAN can also be used to cluster geographical ...

Organizing search results is one of the challenging task of the search engines due to various and dynamic intentions of the queries. As a consequence search engines are not able to understand the exact user context, and thus retrieve large volumes of ...

This paper proposes an online data stream classification that learns with limited labels using selective self-training. Data partitioning steps are proposed to improve stream mining efficiency. Simulation on Cambridge and KDD'99 datasets shows up to ...

Microarray experiments generate large scale data in the form of gene expression values. An unsupervised feature selection approach to perform sample based clustering on gene expression data is proposed. The proposed work uses Particle Swarm Optimization(...

There exists large number of clustering algorithms either for numeric or for categorical data sets. There are relatively less algorithms for clustering mixed attributes. This paper proposes Mutual Information based Weighted Clustering for Mixed ...

Protein-protein interaction (PPI) networks are valuable biological data source which contain rich information useful for protein function prediction. The PPI network data obtained from high-throughput experiments is known to be noisy and incomplete. In ...

In this paper, we test the hypothesis that for a particular item recommendation, it matters more how a friend (i.e., another user who is socially connected) has rated than a random user. To test this, we propose a matrix factorization based ...