Clustering ensemble, also referred to as consensus clustering, has emerged as a method of combining an ensemble of different clusterings to derive a final clustering that is of better quality and robust than any single clustering in the ensemble. Normally clustering ensemble algorithms in the literature combine all the clusterings without learning the ensemble. But by learning the ensemble, one can define the merit of a clustering or even a cluster in it, in forming a quality consensus. In this work, we propose a cluster-level surprisal measure to define the merit of a clustering that reflects both levels of agreement as well as disagreement among clusters. Using the proposed measure of merit, we devise a polynomial heuristics that judiciously selects a subset of clusterings from the ensemble that contribute positively in forming the consensus. We also empirically show that consensus achieved by our proposed method performs better in terms of quality compared to well-known clustering ensemble algorithms on different benchmark datasets.

Matrix factorization (MF) techniques are one of the most succesful realisations of recommender systems based on collaborative filtering/prediction (CF). For instance, in a movie recommender system based on CF, the inputs to the system are user ratings on movies (items) the users have already seen. To predict user preferences on movies they have not yet watched one needs to understand the patterns in the partially observed rating matrix. It is possible to visualize this setting as a matrix completion problem, i.e., completing entries in a partially observed data matrix. Then the objective is to compute user latent factor and item latent factor such that the rating matrix is completed. The factorization is usually accomplished by minimizing an objective function using gradient descent or its variants such as conjugate gradient or stochastic gradient descent. In this paper we make use of a particular MF technique called Maximum Margin Matrix Factorization (MMMF) and show that it is suitable for multi-level discrete rating matrix. The factorization is accomplished by minimizing the hinge loss objective function. We propose to improve the gradient search by combining a component of particle Swarm Optimisation (PSO) search. Though earlier attempts of improving PSO search by adding gradient information exist, the main objective of the present work is to improvise gradient/stochastic-gradient search. Our proposed algorithm finds better minimizing points early (fewer number of iterations) not only for the loss function but also for other performance metrics of collaborative filtering such as RMSE and MAE. There has not been any earlier attempt to combine particle swarm optimisation with maximum margin matrix factorisation for collaborative filtering.

Abstract Recently, researchers have proposed efficient detection mechanisms for masquerade attacks. Most of these techniques use machine learning methods to learn the behavioral patterns of users and to check if an observed behavior conforms to the learnt behavior of a user. Masquerade attack is detected when the observed behavior, reportedly of a specific user, does not match with the learnt pattern of this user's past data. A major shortcoming in this process is that the user may legitimately deviate temporarily from its past behavior. If the deviation is large and near‐permanent, it is desirable that such deviations are captured in a detection mechanism. We propose, in this paper, a method that takes into consideration this aspect of user behavior while detecting masquerade attacks. Our scheme is based on the premise that the commands used by a legitimate user or an attacker may differ from the trained signature. But the deviation of the legitimate user is momentary whereas that of an attacker persists longer. By introducing this novel concept in the detection mechanism, the performance improves. We show this empirically using several benchmark datasets. Copyright © 2010 John Wiley & Sons, Ltd.

In electronic double auctions, property of incentive compatibility is very important. Incentive compatibility ensures that truthful bidding is the dominant strategy. Other important properties in electronic auctions are budget balance (BB) and individual rational (IR). The former ensures that the auction does not run in loss whereas the latter ensures voluntary participation. However these can be achieved only after sacrificing efficiency. The mechanisms based on uniform clearing price have been proposed in literature. Such mechanisms satisfy the properties of BB and IR. They are incentive compatible. However uniform price auction mechanism suffers from the problem of demand shading. Due to demand reduction, agents can acquire units at a lower price. This affects the property of incentive compatibility. Another problem with this approach is that it is not false name proof, meaning that agents can submit bids under different names to improve their utility. In electronic markets, where bids and asks are submitted remotely this property is very important. In this paper we propose discriminatory price mechanism, which is strategy proof, individually rational and budget balance. It is also false name proof, meaning agents cannot improve their utility by submitting false name bids.

The research in qualitative reasoning and in spatial CSP is always investigated in the backdrop of its temporal counterpart - qualitative temporal reasoning and TCSP. Unlike the case of interval algebra (IA), the composition table of RCC, IA's so-called spatial counterpart, is in general neither complete nor extensional, the compositional consistency can be still a valid reasoning mechanism. Even in such a restricted situation, many of the known properties of IA have not been investigated for validity in the context of RCC. We address, in this paper two such properties-convexity and minimality. The importance of minimality cannot be underestimated as in a minimal network every label is feasible and hence determining all the consistent scenarios can be accomplished very efficiently. It is known that path consistency does not yield a minimal network for tractable classes of RCC-8. We represent RCC-8 relations as a partially ordered set and exploit the properties of partial ordering to derive very interesting theoretical results. We show here that there exists a convex class of relations of RCC-8 for which path consistency yields a minimal network. Our results are very important as it gives a sufficient condition for minimality and useful to generate all consistent scenarios whenever compositional consistency is a valid reasoning mechanism

Colour and texture are the most common features used in CBIR systems today. In this paper, we wish to investigate structural methods of texture analysis for CBIR in view of their closeness to human perception and description of texture. In structural analysis, local patterns are the key (as is the case with humans), and when used as features may be expected to return more relevant images in CBIR. One method to describe local patterns in computationally simple terms is texture spectrum proposed by He and Wang. In this paper, we propose two additional characterizations of local patterns. The first is an extension of He and Wang's texture spectrum to larger and more meaningful windows, along with new structural features that capture local patterns such as horizontal and vertical stripes, alternating dark and bright spots, etc. The second is a new method that characterizes patterns as contrast variations in 5 /spl times/ 5 windows. We apply the new texture characterizations to develop a CBIR application and tested their performance on two databases containing remote sensing images. Our results show accuracies that range from 60% to 100% depending on the query image and the features contained therein. These results indicate that our texture features are useful in retrieving images appropriate for different remote sensing applications.

With IP multicast not gaining wide acceptance, researchers turned to alternative multicast mechanisms like application level multicast. Application level multicast (ALM) protocols arrange the participating hosts into an overlay topology; maintain it and distribute data over that topology. ALM topology building algorithms define a definite relationship among the participating members and thereby create topologies like tree, mesh, hierarchy etc. We propose a new ALM protocol, simulate the protocols and compare the results.

This paper discusses a new similarity measure for the anomaly‐based intrusion detection scheme using sequences of system calls. With the increasing frequency of new attacks, it is getting difficult to update the signatures database for misuse‐based intrusion detection system (IDS). While anomaly‐based IDS has a very important role to play, the high rate of false positives remains a cause for concern. Defines a similarity measure that considers the number of similar system calls, frequencies of system calls and ordering‐of‐system calls made by the processes to calculate the similarity between the processes. Proposes the use of Kendall Tau distance to calculate the similarity in terms of ordering of system calls in the process. The k nearest neighbor (kNN) classifier is used to categorize a process as either normal or abnormal. The experimental results, performed on 1998 DARPA data, are very promising and show that the proposed scheme results in a high detection rate and low rate of false positives.

A new paradigm of neural network architecture is proposed that works as associative memory along with capabilities of pruning and order-sensitive learning. The network has a composite structure wherein each node of the network is a Hopfield network by itself. The Hopfield network employs an order-sensitive learning technique and converges to user-specified stable states without having any spurious states. This is based on geometrical structure of the network and of the energy function. The network is so designed that it allows pruning in binary order as it progressively carries out associative memory retrieval. The capacity of the network is 2n, where n is the number of basic nodes in the network. The capabilities of the network are demonstrated by experimenting on three different application areas, namely a Library Database, a Protein Structure Database and Natural Language Understanding.

In this paper, an efficient algorithm to compute the set of prime implicants of a propositional formula in Conjunctive Normal Form (CNF) is presented. The proposed algorithm uses a concept of representing the formula as a binary matrix and computing paths through the matrix as implicants. The algorithm finds the prime implicants as the prime paths using the divide-and-conquer technique. The proposed algorithm can be used for knowledge compilation, Clause Maintenance Systems where the knowledge base is propositional formulae. Moreover, the algorithm is easily adaptable to the incremental mode of computation where an earlier formula is updated by a set of clauses.

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.